{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=10, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [0.74081822]\n",
      "Phi(-1.0, 1) = [0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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hbdu2nDlzhiVLluRaAlOXGBcbG8vYsWNp3rw5v/76a9G+jwKYKgGsBDzIse0BUDmvnaWUa6WU7lJK9+IqgFiatW3bFsgdHHfv3s2vv/7KrFmzqFbY4zmlbMsxINmzQxRrhwTjaB+PEBJH+3jWDgnOcyDzmjVr2Lp1K+PHj+fSpUsMHToUq/zGt1hZacfTtG8Pr7yifW/QwChj/gICwMsLrl3Tlvu6dk37uQQngUWOcyrGFUzFOKVQ+sS4tDT45x+4cqXoS7fpGOPCw8NZtWoVAK1bt+bUqVP8+uuvNG3aNNe+RY1xaWlpfPXVV7i4uLBkyRLS0tIYPHhw0fpfACGlNLiRXI0KMQeolzE4WgixDKggpRyeZZ+zgI+UcntBbbm7u8uMRZDzc+HChTx/uCW1WKihP3MpJfb29rRq1Yr9+/cD2lvNzZo1w9ramtOnT2NZ2NgF8v+5KWXE2bO5yiTkRaPRsPnoUeo2b85rgwfz6NEj7ty5w3PPPWeijubNyUkbEHNydISrVws/XghxQkrpbux+ZWnfKHHOkBiXfg7dO28ChsQ5FeOUIilijMsl43dHlxVCCvHw4UPmzJnD0qVLsbOzIyIigurVqxd4TFFi3JEjRzIfHQO8/PLLLF++nFatWhkc40x1B/A8kFnVUAhREWiYvl3RkRCCtm3bEhwcnBlkly1bRlhYGEuXLi1SYFTKgSIMZA66cIE2U6Yw4Isv8D90CIDKlSubPfkDiIzUbXsJoOKckagYpxRJUSdr5JSW9qQu4NGj2ttvetJoNPj7++Pi4sLnn3/OBx98QGhoaKHJHxQc46KiovDw8KBDhw6cPHmS+vXrs3XrVn7//XdatWqld3+zMmoCKISwEkLYApaApRDCVghhBewEmgkh+qR/fTpwRkoZaszz5ySlLJEvY2jbti0PHjzg4sWL3L59m9mzZ9O7d2/+85//GKV9pQwoYCBzxK1bvOPnx79nzOBWXBybNm5k06ZNZuxsbvmVxTJDuaxsVJwzTZxTMU4pVGGTNQqjywoheZBScuPGDUaMGMFzzz3HX3/9xTfffEPt2rWLdHx+saxKlVgaN27M1q1bsbW1Zfr06YSGhtKvXz+j3vE39h3AqUAiMBH4IP3fU6WUd4A+gC9wH/gX8J6Rz12uZB0kPXnyZJKSkli8eLGZe6WUOPkMZD54/Tq//v03s3x8mDXvGlOnfYiVlUWJmmjh6wt2OSo42Nlpt5uZinMmoGKcUiQ5Y1weyZdRVgjJ4vr168yfPx+AZ599luPHj3P06FFefPFFnbqeV4wTIoEHD4aTmJjIu+++S2hoKDNnzsQu547GYO6rxMJerVu3loUJCQkpdJ+y5sGDB9LCwkJ26NBBWlhYyAkTJujcRnn8uZVXKSkp8ssvv5T+/v5SSilTU1NldHS03LxZSjs7KbXPQLQvOzspN282b38zbN4spaOjlEJo33XpFxAsS0AMK+ylYlzeVIxT9BIRIeX27VIGBkoZGCg3jzwm7axTssc46xS5eeSxzH3k9u3a4wqRmJgo58yZI+3s7KSNjY28cOGCwd3dvFnKOnWSJKRJuCLBQ7Zo0UIeOnSo0GMNjXFqLeBSqkqVKri6uhIUFEStWrWYMmWKubuklFB79uyhRYsWjBgxgp9//hkAS0tL6tSpw5QpkJCjTFZCApSUXydPT+1gaI1G++7pae4eKaaiYpyiFx1WCMmUsUJIPqSU7Ny5E1dXV6ZOnUqXLl0ICQmhSRPDSnzeuXOHoKCh3Lr1FGBJjRrurF7dkRMnTtCxY0eD2i4KlQCWYm3atAFg3rx5VK6cZ0UdpRwLDQ3ljTfeoGvXriQnJ7Nz5062bduWbZ9SONFCKUdUjFN0VgwrhCQkJDBixAjs7OzYv38/O3bsMGiiXEpKCsuWLcPFxYU1a9YghGDUqFGEh4czZMgQk01yUglgKZWSksKhQ4dwd3dnwIAB5u6OUgJFR0dz/Phx/Pz8OH/+PL179841gLikTrRQFBXjFL0YaYWQmJgYZs6cSUpKChUrVuTAgQOcPn3a4ElIe/fupUWLFowePZrY2Fg6d+7M2bNnWbp0qclrW6oEsJRatGgRV65c4YsvviixdcAU03r8+DELFy7Ex8cHgE6dOhEZGcmYMWOwzhEUM5TgiRZKOadinKIXA1cISU1NZeXKlTg7OzNr1iyCgoIAaNKkSf4F8YsgPDycnj170qVLFy5cuEDDhg358ccf2bNnj9lqVaoEsBSJiYlhy5YtTJo0iWnTpjF27NjMivlK+SWlZNu2bbi6ujJhwgTOnj2bWYajsMdmnp6wdq228KgQ2ve1a0v2WLvClk66ePEiPXv2NEfXFAOpGKcYzIBVkA4cOECrVq0YMWIELVq04PTp03Tq1Mmg7jx8+JAJEybg5ubGTz/9RKVKlViwYAHnz5+nR48eeV7cmGoJTMPXa1JM5rfffuP999+nVq1ajBkzJnMaulJ+hYSEMGTIEI4cOULz5s3Zt28fr732mk5teHqW7IQvq4ylkzImrmQsnQTwxhv3mDVrFitXriRVh5IOSsmhYpxiMCsrbV3ALCuEeHaIyp3wZbC0BGdnNBYWeHt7ExcXx/bt23nrrbcMuvOs0WjYtGkTEydO5ObNmwAMHDiQuXPnUqdOnXyPKyjGZcTpqKgo5syZo3ffMqgEsBTx8PDAw8PD3N1QSgApJUIIrKysuHr1KmvWrOHjjz8u8ysk5DdreeTIh4Az9+/fx8LCgsGDB/PVV1+ZpY+K/lSMU4yiWTN48EBb5LmAZeLikpNZfOAAIxcupLqFBTt27KBOnTrY2toadPrjx4/z6aefZq5l3bZtW5YvX16kOoEFVWbw9IRVq1bh7e1tlGLr6hGwopQicXFxTJ8+nQ8//BAAFxcXIiIi8PLyKvPJH+Q/O/n+/Urcv3+f1157jVOnTrF27VrTdkxRlJKjkBVCpKUlm48cofHYsfh88w0/794NQIMGDQxK/qKjo+nfvz9t27blr7/+ok6dOmzcuFGnItH5V2bQJnz29vZ0796dCxcu6N3PDOoOoKKUAhqNho0bNzJ58mRu3LjBe++9R3JyMtbW1lTIMXutLHNwyHvxdCurG+zc+RPdu3dXEwYURXmyQkjTphAVpa3zl5LC/y5dYtSqVRw7eRJ3d3e+/+GHzFVn9PX48WOWLFmCr68v8fHxWFtb4+3tzeTJk6lUqZJObeUX46pWfQhUpW/fvvTt29eg/mZQdwAVpYQLDQ3F3d2djz76CAcHB/744w+2bNmS78zesmzcuPtYWiZl21ahQgrr1j3Dm2++qZI/RVGys7KCBg20dwRfeYW5u3cTcf0633zzDcePHzco+ZNS8sMPP+Dm5sbkyZOJj4+nd+/eXLhwgblz5+qc/EHelRkggb59T+ndz/yoBFBRSqi09LErtWrVQkrJt99+y7Fjxwy+Wi2NEhISmDNnDhMm1Cct7SPgGiCpVy8Nf/8K9O+vHmYoipJbcnIyn3/+OWFhYQCsXLmSsLAwPvroIyws9E+Bzp8/T+fOnXnrrbeIiIjAzc2Nffv2sXPnToOKRHt6wqBBxxEiEtBQuXIMa9ZoWLv2Fb3bzI+KmopSwty/f585c+Zw9OhRjh49SvXq1Tl58mS5vLul0Wgyy4JERWln8fXuncjChck4Owug7I97VBRFd1JKdu/ezdixYwkPDyc5OZkpU6YUOAO3KGJiYvDx8WHlypWkpaVRrVo1Zs6cybBhwwyqEwja4ucVKlTA01MQGjoYPz8/3NzcDGqzIGXmDqAxZsSUJ+rnVfKkpKTwxRdf0KhRI5YsWUKzZs1ITEwEKJfJ3x9//EG7du344IMPiIqKomXLlhw4cICdO3fi7Oxs7u6ZnPqb1Y36eZVfoaGhdOvWjR49emBhYcGvv/5q8FrSWQtEf/HFF0gpGT58OOHh4YwcOdKg5O/y5cv07t2bESNGANolEH/77bdiTf6gjNwBtLS0JCUlpVyOidJXamqqwVcrivFERETQvXt3QkND6dSpE35+frRo0cLc3TKLq1evMmHCBAIDAwGoXbs2c+fOpX///uVipnNeVIzTnYpx5dfatWs5duwYfn5+jBgxwuC/m0OHDjFq1CjOnDkDwCuvvMKyZct4/vnnc++cmvpk0klysnZpurp1tQWqc/w+Pnr0iLlz5+Ln54e1tTXTp083qJ+6KhN/HZUrV+bhw4fY29ubuyulxqNHjwyudaQYLj4+nooVK1KvXj0aNmzIggUL8q0OX9Y9fPiQuXPnsnTpUpKSkrC1teWzzz5jwoQJeg2mLktUjNOdinHlR1paGt988w1NmjShQ4cOzJgxg4kTJ1KrVi2D2r169Srjxo3j+++/B8DJyYnFixfnXSRaSjh3TluAWtupJ1+7dQtOndKWpWnWDITg8OHDeHh4cOPGDQYMGMC8efMMfjytK5M+AhZCNBVCHBBCPBBCXBJCvGWMdqtXr879+/e5e/cuycnJ6tZ/AaSUJCQkcPfuXWrWrGnu7pRbt27dYsiQITRp0oS4uDisra35+eef6dmzZ7lL/lJTU1mzZg2NGjViwYIFJCUl4enpSVhYGLNnzy51yV9xxDkV44pOxbjyJSgoCHd3d7y8vNi0aRMAVatWNSj5i4+PZ9q0aTRp0oTvv/8eOzs75syZQ0hICG+//Xbeyd/Ro09WH8lZfDpjW3g4KYcPg5Q0aNCAxo0b8+eff7J+/XqTJ39gwjuAQggrYBewGngd6Aj8JIRoJaUMM6RtGxsbHBwciImJ4erVq5mzJ5W82djY8Mwzz6irYzN4/PgxS5cuZe7cuSQmJjJixAg0Go25u2U2e/fuxdvbm3PnzgHQvn17/Pz8aNOmjZl7pp/iinMqxulGxbiyLzIykvHjx/Pdd99Rv359tm7dyrvvvmtQm1JKtmzZwvjx47l+/ToA77//PgsWLKBevXr5H3juXKGrjkTHxDDx22+5ERvL3k2bcHj+eQ4ePGhQfw0mpTTJC2gGxAEiy7a9wOyCjmvdurVUlLLgxo0b0tHRUQKyV69e8uLFi+bukslt3iylo6OUQmikre1NCR4SkE5OTjIwMFBqNBqjnQsIliaKbxkvfeKcinGKorvly5dLW1tbOWPGDBkfH29we8HBwbJ9+/YSkIBs3bq1PHLkSOEHpqRIuX27lIGBma/NI49JR/s4KYRGOtjHyXfbrZCVbG2ltZWVnNCrl0z+7jvtcQYyNMaZewygQBswFaXMunnzJrVr1+aZZ56he/fu9OnTh06dOpm7WyYXEACDB0sSEwUgePz4GeBr+vV7j/XrO5fluzUqzimKgaSUbNu2DQsLC9555x2GDh1Kr169cHBwMKjdW7duMWXKFL755huklNSqVYt58+YxcODAotUJTC9PlSEgqD5ea9xJSNamV5F3KxJ5dyAvNLhB4JhnaFi7tnZpuqgobYFqMzLlGMCLwG1gnBCighCiM9rHI7lqXgshvIQQwUKI4Dt37piwi4piPJGRkXh6etKwYUP++ecfhBB8+eWX5TL5S0pK4pNPHqQnf1nZ8eefPctS8lekOKdinKIU3enTp3nllVfo168fX3/9NQAVKlQwKPlLTk5m8eLFuLi4sG7dOqysrPD29iYsLIz//ve/RS8SHR2d7dHvlC3NM5O/Jypy79EkbfIH2v2jo/Xuu7GYLAGUUqYAvYHuwE3AGwgE/slj37VSSncppbsaxKuUNo8ePWLq1Kk0btyYHTt2MGbMGJ5++mlzd8sspJTs2LEDNzc3YmMr57lPfoufl0ZFjXMqxilK4e7cucPQoUNp3bo1ISEhrF69mt27dxvc7u7du2nWrBmfffYZDx8+pHv37pw7d45FixZRtWpV3RpLTs785/24OK7dfSrP3SLv5bjXlZKia7eNzqSPgKWUZ9BeDQMghPgD2GDKPihKcXr06BFNmjQhOjqa999/n3nz5hn8iKK0OnHiBGPHjuX3338HwMrqBqmpz+bar6z9eFScUxTjOH78OOvWrePTTz9l+vTpVKtWzaD2QkNDGTt2LL/++isAjRs3ZsmSJXTt2lX/Rq2tSdNoWLt/P9O++w54FXDKtZtDjYTsGypU0P+cRmLSBFAI8TwQhvbO43CgDrDelH1QlOIQEhK1kGIuAAAgAElEQVSCq6srlStXZvTo0fz73//mX//6l7m7ZRbXr19nypQpbNy4ESklNWrUYObMmVSu/AzDhkFCljhoZ6dd/LwsUXFOUfS3d+9eIiIiGDp0KN27dyc8PBwnJyeD2nzw4AGzZs1i+fLlpKamUqVKFWbMmMEnn3xicJHoaEtLuk6YwJlr1+jo6kqX588yZ0e9bI+B7axT8fU4++QgS0ttcWhzM2QGia4v4HPgPtpZcr8CjQo7Rs2QU0qyixcvyp49e0pABgcHm7s7ZhUfHy99fHyknZ2dBGSFChWkt7e3vH//fuY+T2YBa983by6+/mCGWcBSjzinYpyiSBkeHi579OghAdm8eXOZYoRZsqmpqfKrr76SNWvWlIAUQshBgwbJW7duGdz248ePted4/Fj2btNGbhs7Vmq++y7XLGBH+zi5eeSxbLOE5fbtJWIWsNC2UXK5u7vL4OBgc3dDUbKJiYlh1qxZfPnllzz11FNMnjyZ0aNHl6XJDEWm0WgICAhg0qRJmbWz3n77bRYuXEjDhg3N1i8hxAkppbvZOlBEKsYp5dnDhw/x9fVlyZIl2NjYMG3aNEaNGoWNjY1B7R45coRRo0Zx8uRJQFtjdPny5bzwwgsGtRsfH8+CBQvw9/fn77//pnr16nD27JMi0IWxtNSuCNK8uUH9AMNjnLnLwChKqZOSkkLLli25fv06gwcPZubMmTzzzDPm7pZZHDlyhDFjxpCRwLRu3Ro/Pz/+/e9/m7lniqKUBleuXGHx4sV8+OGHzJs3j9oZM2X1FBUVxYQJE9iyZQsA9erV4/PPP6dfv34GrbIkpWTr1q2MHz+ef/75Bw8PD1IyJnI0awYPHhRaDBpLS6hVS7t/CaASQEUpAiklhw8fpmPHjlSoUIGFCxfi5uZGcyNcxZVGERERjB8/nu3btwNQt25d5s6dy4cfflj08gmKopRLf/75J/v372fq1Km0aNGCiIgIgyfLJSYm8vnnnzN//nwSExOxtbVl/PjxjB8/nooVKxrUdnx8PF26dOHo0aO88MILbN26lfbt2z/ZQQho3z7/tYCtrLTLxWVZC7hEMOT5sSleanyMYm6nTp2SnTp1koD86aefzN0ds4qNjZXjxo2T1tbWEpB2dnbSx8dHxsXFmbtruWCmMYC6vlSMU8qL69evyw8++EACsm7dutnGB+tLo9HIwMBA6eDgkLmKR9++feWVK1cMbjsxMTHz34MHD5Zff/21TEtLK/iglBQpIyKkPHJEyoMHte8REUYZ85eToTHO7MGvsJcKjoq5REdHy48//lgKIWSNGjXkihUrZHJysrm7ZRYpKSly5cqV0t7ePjPI9u/fX0ZFRZm7a/lSCaCilAyJiYnS19dXVqxYUVpbW8tJkybJR48eGdzu6dOnZceOHTNjUosWLeShQ4cMbjcpKUkuXrxY1qxZU4aFhRncXnExNMapR8CKkgcpJf/5z3+4dOkSY8aMYerUqQbXoCqt9uzZg7e3NyEhIQB06NABPz8/3N1L/PwKRVFKgNjYWObPn0/nzp1ZtGgRzz33nEHt3b17l2nTprF27Vo0Gg01atTA19eXQYMGYWlpWfSGUlO1S7JFR2sLOltb88vFi4xZsICwsDC6du2qW3uljEoAFSWdlJLt27fTo0cPbGxsWLVqFc8++yyNGjUyd9fM4ty5c3z22Wf89ttvADz33HMsXLiQt99+26DB1IqilH3nzp3D39+fRYsWUbt2bUJCQqhXr55BbaakpLBy5Up8fHyIjY3F0tKSTz/9FB8fH90u0KXMNV5Po9Hw9qJF7AoOxqVuXX5esYLuw4eXnPF6xUCN1lYU4NixY7Rr146+ffsSEBAAQMeOHctl8nf79m2GDRtGixYt+O2336hSpQqff/45ISEh9OnTRyV/iqLkKyYmhpEjR9KyZUv8/f25fPkygMHJ3759+2jZsiWjR48mNjaWzp07c+bMGZYtW6Z78nf0aGbZlsTERAAsLCx43tGRRR9+yNlFi+hep452P1myS+UZQiWASrl29epV3nvvPV566SUiIyPx9/dn4MCB5u6WWSQlJbFw4UKcnZ1ZvXo1QgiGDx/OpUuX+Oyzzwyuy6UoStmVmprKypUrcXZ2ZuXKlQwZMoTw8HCDL6IvXbpEr1696Ny5MyEhITRs2JBdu3axZ88eXF1ddW/w3Dm4fZu0lBTWHTiA04gRHDx3DoBZ/frh3aMH1lZW2lm8t29r9y+jVAKolGv9+/fnxx9/ZPr06YSHhzNw4MByV8ZESsm2bdto2rQpEyZM4OHDh3Tt2pUzZ87w0ktf8uKLNbGwACcnSL85qiiKkk1KSgoLFiygRYsWnDp1ii+//JIaNWro3d6jR4+YOHEibm5u/Pjjj1SqVIn58+dz/vx5evbsqd+TiNRUCA/n6PnztJk0iUGrV1P1qY/xXD4Wi37v4DS8GwFB9Z/sn5amvVOYmqr391GiGTKDxBQvNUNOMaaMpYFu374tpZTy3LlzJXoma07GXkrtr7/+ku3bt8+cRefm5ib37NmTeS47Oym1z0C0Lzu74l2+zZhQs4AVpVhFRETIYcOGZZZLuX79utRoNAa1mZaWJr28DksLi0gJaRKuyA4dVsvo6GhjdFgOf+MNCch6NWrI4V02SDvrlOwxzjol+9Jt27dry7iUQIbGuPJ1q0Mp1/bt20erVq0YPHgwGzZsAMDNzc3gsSmmEhAAXl5w7Zo2VF27pv2sz125f/75hw8//JA2bdpw9OhR7O3tWbVqFadPn6ZLly4ATJkCCQnZj0tI0G5XFMWEUlPhyhXtmLSDB7XvV66Y7c5UfHw806ZNo2nTpmzYsIETJ04A2oLwhowRPn78OM7OM1i7tjUaTX20DymdOHFiCAcO1NG73cTERDQaDURH83z9+kx9+21Clyxh94k+JCRnnwubkGzFlC1ZCvynpWlnCZdBKgFUyrzQ0FC6d+9O586diYuLY9u2bXh7e5u7WwQEaB+rFvXxqjESsvj4eGbMmIGLiwubN2/G2tqa8ePHc+nSJYYOHYqV1ZNgGBmZdxv5bVcUxcik1K4z++OPcOqUNhG5e1f7fuqUdvvZsyabqCClJCAggMaNGzNnzhz69OnDxYsXs6+KkUNR4tyNGzcYMGAAbdu2JSLiYyD7yh36XnjK9MoOrq6urF+/HpKTGfL668x+7z0q2toSec8uz+Nybc9Y8q2MUWVglDLPx8eHI0eOsHDhQkaOHImtra25u5R5Ny8jocu4mwfg6Zn3MYYkZBqNho0bNzJ58mRu3LgBwDvvvMOCBQvyrcnl4KDtV17bFUUpZhmzVfNbXzZjW3i4dh3a9u2LvWSJlJIVK1ZQp04dAgMDeemllwrcv7A49/jxY5YuXYqvry9xcXFYW1uTnOyYZ1u6XnieOXOGUaNGcejQIZo3b46zs7M2C83CoUYC1+7mXibOoUaOK+0KFXQ7eSmh7gAqZU5ycjJLlizJLFzs5+dHeHg448aNKxHJH+h3Ny+/xKuwhOzQoUO4u7vz0UcfcePGDdzd3QkKCmLbtm0FFmT19QW7HBfCdnba7YqiFLP02ap5Jn9ZFfNs1Zs3bzJ8+HBu376NhYUFu3bt4vjx44Umf5B/nJs8WbJr1y7c3NyYNGkScXFx9OrVi5CQEBwd805idbnwnDt3Lq1ateLs2bOsXLmSkydP0qFDB6hbF7IUdvb1OIuddfbH6HbWqfh6nH2ywdJSe1wZpBJApdR78ohBUqtWAvXrT2Ds2LEEBgYC2nEptWrVMm8nc9Dnbp6uCdmlS5d46623ePXVVzl16hT16tVj06ZNHD9+nJdffrnQPnp6wtq14OiovbHg6Kj9nN8dSkVRjCR9tmrW5C8gqD5Ow7uZbLZqcnIyixYtwsXFha+//pqgoCAAatWqVeRKCfnHOUnv3r2JiIjA1dWVffv28cMPP9CwYUO9LzxTUlJ4/PgxAC1atGDEiBGEhYUxbNiwJ0Nb6tfPdoxnhyjWDgnG0T4eISSO9vGsHRKMZ4eo7I3nOK6sELKEFzl0d3eXwcHB5u6GUkLlfMQAIEQin312kYULW5qvY4Vwcsr78aqjI1y9mv9xAQHaq+rISO0Vsa9v7oTs/v37zJkzhy+++IKUlBTs7OyYOHEi3t7e2OWMrGWYEOKElLLEr1enYpySy5Ur2jF+6QlgQFB9vNa4Z5uwYGedmj1ZsbSEVq2gQQODT797927GjBlDeHg43bt3x8/PDxcXF53byS/OwVWefroVs2bNYujQoVTI8Yi1KHEuq/379zN69GjefvttZs2aVXCnzp7NlVzny9ISnJ2hefPC9zUDQ2OcSe8ACiGchBC/CCHuCyFuCiFWCCHUOERFb3k9YpDyKQIDjZ/86TppoyD6XuV6emoTRI1G+541KKakpLBixQqcnZ3x8/MjNTWVgQMHEh4ezrRp08pV8mdOKs4pBouOzpagTNnS3CSzVTNi3JtvduXKlYOMG3eKn3/+Wa/kDzLiXM6bTPH85z8HCA8PZ+TIkbmSPyg4zmV1+fJlevfuzeuvv05iYiIvvvhi4Z1q1gxq1cr2KDhPlpba/Zo1K7zNUsrUj4BXAreBOkBLoCMw3MR9UMqAhIQEZs+eTWRk3newjTlTNSAA7O3hgw+MU4IFjPt4VUrJ7t27ef755xk5ciT37t2jY8eOBAcH4+/vT90yOn6lIGlFubovPirOKYZJTs72sbhnq371VTy2tnF88IFMv2NnQWrqs3z5ZUuDLnSfffYQNWpMAq4CGmxsbjJ37j327/8v9vb2+jcM+Pv74+rqyv79+5k3bx7nz5+nR48ehR8ohHbCjLOzNsnLmQhaWT2582eCiTXmZOoEsAEQKKV8LKW8CewB3EzcB6UU02g0bN68mcaNGzN9+nQqV47Ncz9jzFTNmvjdu5f764bWxCvqVW5Bzp49S5cuXXjzzTcJDQ2lUaNG7Nixg4MHD/LCCy/o37lSLDo6mjZt2pizCyrOKYaxts72Mdes1Py26zhbddMmDZUqJeLlZUdSUiUge7Kjb4y7evUqffv25dVXXyUqagGOjq/w/fc7SUx8hkmT9A/OGo2GuLg4AFq3bs17771HWFgYEydO1G2CnxDax7o9e2ofm9etCzVrat9bttRub968TCd/gGlXAgGGABsBO+BZ4BzwVh77eQHBQLCDg4PRqmYrpdsff/whX3zxRQnI1q1by99//73YVqvIq928XkIY53vT1c2bN6WXl5e0sLCQgHz66aeln5+fTEpKMk+HzCw1NVUGBQVlfl6xYoXZVgIpSpxTMU4pUESEdgWK9NUoNo88ZvQVK6ZNuyCFSDBqjIuLi5PTpk2Ttra2EpB2dnZy9uzZMiEhQY8fQnbHjx+Xbdu2lZ6enga3VVYYGuNMHRibAieAVLRLT60nfSJKfi+1TJKSYdWqVfLZZ5+VGzdulGlpaZnbjb08mpTadgoLjKDdz5QSExPlvHnzZOXKlSUgLS0t5ciRI+Xdu3dN25ESZP/+/fL555+XFhYWMiwsLHO7GRNAneKcinFKLikp2RLAjCTQ0T5OCqGRjvZx2ZO/jAQwJaXIp6hSJcZoMU6j0chvv/1W1qtXL3NZSQ8PD6MssxkdHS0HDBggAVm7dm25YcMGg9ssK0pNAoj2cfM1YApgA9QAdgELCzpOBcfyKzY2Vo4bN05+/fXXUkopU1JSZFxcnEnOLUThgdGU6+JqNBq5detW6ejomBlg33zzTXnhwgXTdKAECg0NlT169JCAdHJykoGBgdnWITVHAqhPnFMxTsnTmTO5ksB8X9u3a/cvQHx8vJw5c2bmWt9CaIwS44KDg7OtJ/7CCy9kuxtviF9++UVWqlRJWltbywkTJsiHDx8apd2ywtAYZ8oxgNUBB2CFlDJJSnkP8Ae6mbAPSimQmprK6tWrcXZ2ZtGiRZkFna2srKhYMXfV9uJQ2BjCGjVMVxPv+PHjtG/fnvfee49r167RvHlz9u3bx08//USTJk2KvwMl0MOHD3F3d+fQoUPMnz+fCxcu0LdvX4PWITUSFecU4zDSbFUpJYGBgTRt2pQZM2Zw4MABABwcCv5bKSzG3b59m0GDBvHiiy9y9OhRatasyddff81ff/1VpDqj+ZFS8uDBAwBatWrFm2++yfnz55k/fz6VK1fWu10lD4Zkj7q+gAhgItol6J4GdgLfFnSMujouX37//Xfp5uYmAdmxY0d54sQJs/QjvzGANWqY7q7ftWvX5Pvvv595ZV2rVi25du1amZqaapoOlDBJSUly27ZtmZ937twpb926le/+mO8RsE5xTsU4JV8azZM7gTnvBu7Y8eTOX5Y731n9/fffsmPHjhKQLVq0kIcOHcr8mr4xLikpSS5evFhWqVJFAtLKykp6e3vL2NhYg7/d8+fPy9dff122b98+2918JW+GxjhTB8aWwCHgPnAXCASeKegYFRzLh4w/9j179siGDRvKHTt2mD0AFMfYwqJ49OiRnDJlSuZAahsbGzlx4kT54MED03SghNFoNHLXrl3S2dlZAvLYsWNFOs6MCaBOcU7FuHIgJUU7QePIESkPHNC+R0QUfcxe1uMPHizy8WvXrpU1atSQq1evzvPCUdcYt3v3buni4pJ5UdqtWzcZGhpatO+hADExMXLUqFHS0tJSPv3003Lp0qXl9kJXF4bGOLUSiGJWd+7cwcfHh2rVqjFnzhxAW9A4r+KgZV1aWhrr169n6tSp3Lx5E4B+/foxf/58nJyczNs5Mzl9+jRjx47l4MGDNGnShMWLF9O1a9ciPepVK4EoZieldo3e8HDt56z1KTMe7To7ax/fGmH4QkpKCqtWraJKlSoMHDiQtLQ0Hj58SLVq1Qxq9+LFi4wdO5ZffvkFgMaNG7NkyRK6du1qcJ+Dg4N54403uH//Pl5eXsyePdvgGoHlRalaCURRMiQlJbFo0SKcnZ1Zs2YNiYmJmV8rj8nfgQMHaN26NYMGDeLmzZv861//4o8//mDr1q3lNvlLTk6mW7dunDlzhhUrVnDmzBm6detWEsb5KUrhpISjR58sO5azOHnGtvBw7X4G3ozZt28fLVu2ZNSoUfz2228AWFpaGpT8PXjwAG9vb5o1a8Yvv/xClSpVWLx4MWfOnDE4+YuN1dZwdXNzo3Pnzpw8eZJVq1ap5M+EVAKomFxQUBCurq6MGzeO9u3bc/bsWRYvXmzubplFWFgYvXr14j//+Q9///03Dg4OfPvttxw7dox27dqZu3sml5iYyIoVK0hNTcXa2prt27dz6dIlRowYUS4vDJRS7Nw5uH278DVn09K0+507p9dpIiIi6NWrF507dyYpKYldu3bx7bff6tXWky6l8fXXX2cuK5mWlsagQYMIDw9n7NixWOcoVK2La9eu8e677/LCCy/w+PFjnnrqKb799ltatGhhUJ8V3akEUDGZjOW5atSoQZUqVdi7dy+7d++madOmRl1ntzSIiYlh9OjRuLm58eOPP1KpUiV8fX0JDQ3Fw8Oj3N3l0mg0BAQE0LhxY0aOHMnevXsBaNeuHU8//bSZe6coOkpNfXLnL11AUH2chnfDot87OA3vRkBQ/Sf7Z9wJTE3V+VQREREcOHCA+fPnc/78eXr27GlQ/Dh69Cht2rRh8ODB3Llzh/bt2xMcHMxXX31FrVq19G43Pj6eGTNm0KRJE37++WcGDhyod1uKkRRloCCwGu2gz7p5fK0xkAwsN2QwYn4vNUC69IuKipL9+/eX/fr1y9yWdYJHca3mURIlJyfLpUuXymrVqklACiHkoEGD5I0bN8zdNbM5cuSIbNOmTWYNsawzFQ2BmSaB6PpSMa4MKsaVPNLS0qS/v7+cN29e5raYmBiDuxwVFSU9PDwyJ3jUq1dPBgQEGGUy3tWrVzOLRL/33nsyMjLS4DYVw2NcURPAAem/FL3z+NovaGe6VTOkI/m9VHAsveLi4uT06dPlU089lTmTNesKHhnyW3VD11U2zDVrtyg2b9bImjXjJaRJuCLBQ3bq1EmePn3a3F0zq7S0NPn888/LunXryvXr1+f5+6EvlQAqZnPkSLaSLY72cXnHOPu47KVdjhwpsFkfnzBpbR0tIU3a2NyQGzca/veSkJAgZ8+eLe3s7DKrDkybNk3novt5xd979+5JKbV/54MGDZK///67wf1VnjBVAtg4PQGcm2N79/Ttww3pREEvFRxLpz/++EPWrVtXAvLdd9+VEQVc2ea36oYua1CW5LuIc+delRYWidn6ZmOTIjdvLp91rh48eCCnT58u79+/L6WU8uLFi8WywotKABWzOXAgW2KX36obQmiyJ4AHD+bZ3I0bN2T79islxBktxmk0Grlt27Zsqwv17dtXXrlyRee28oq/lpaP5VNPfSxv3rypXweVQhka44o6BjAMiAHaZGwQQlQA/NAudL5Gh6fOShkWHx8PQKNGjXB1deXIkSN89913NGjQIN9j8lt1o7DVOLKaMgUSErJvS0jQbjeXGzduMGjQICZPlmg0ttm+lpRkxZQp5WucX2pqKmvWrKFRo0bMmjWLX3/9FQAXFxeTrfCiKCaRY5KEQ42EPHfLtT2fiU6xsbH88Ud3IPvfib4x7syZM3Tq1Im+ffty7do1nn/+eQ4ePEhgYKBeVQfyir9paTZYWS3E1tY274MUsytSApieaf4JuIsno0tHAS7AaCllIdOclLLu0qVL9OnTh06dOqHRaKhZsyb79u2jffv2hR7r6wt2dtm32dlpt+eU32SRyMi8285ve3FKTEzE19cXZ2dn1q1bh3ZlsNzM0Tdz2bt3L61atWLo0KE0adKEv/76Cw8PD90aSU2FK1e0JTMOHtS+X7mi18B5RSlWdetmW8LN1+MsdtbZf0/trFPx9Tj7ZIOlJdStS0AAODpKLCwkVarEEBBA+pKP9cmLLnHk7t27DBs2jFatWnHo0CFq1KjBqlWrOHHiBK+88ooO32DR+hAXV52qVavq3W65Y+IYp8ss4D+BqkBjIUQtYBrwg5Ty/4qlZ0qpEBsby2effYarqyu//fYbPXr0yJztW1Sento1Jx0dtbVQHR3zXoMyIAC8vODaNe1DhmvXtJ8DAoxzFzEvusxOllKyZcsWmjRpwtSpU4mPj6dnz57UrZv3z8PQvpUmy5cvJyEhge+//57Dhw/z4osvFv1gKeHsWfjxRzh1CqKj4e5d7fupU9rtZ88aXEdNUYymfvZkzbNDFGuHBONoH48QEkf7eNYOCcazQ1S2/QKOODBokIbISIGUgkePqjN4sEyPcXk/MShKHElJSWH58uU4OzuzevVqhBB8+umnhIeHU7nyUBo1stK7AsO9e/eKLf6WG2aKcbokgMfS39sAcwEbwNuovVFKlVOnTtGoUSP8/Pz48MMPCQsLY+rUqXrVa/P0hKtXQaPRvue1AHlBj3l1uYtYVAUlnDkdO3aMl156iffff5/IyEhatGjB//3f/7Fr1y4WLqxg9L6VdHfv3uXTTz/l8uXLAKxbt46QkBD69OmjW4kKadpiuopiFFZW2hU+stwF9OwQxdWVv6D57nuurvwle/JnaUnsM88wbMQDHj/O/t9yYqIwKMZlLRAdGxvLa6+9xt9//82yZcv45ZdqRY5xOT148IBx48ZRv359hg6NLHcxzmjMGON0SQD/AjTAIOAjYKmUMsJoPVFKBSklt27dAsDV1ZVu3bpx8uRJ1q1bR926dYv13AU95i3qXURdFGVc4dWrV3nvvfd46aWX+PPPP6lduzbr1q3jxIkTdOrUCSievpVUycnJ+Pn50ahRI1auXMnhw4cBeOaZZ7CxsdG9QRMV01UUo2vWDGrVypYE5snSEmrVIq1xYx49yrvmpT4x7vLly/Tu3ZvOnTsTEhLCc889xw8//MDevXtxc3MD9Bs7rdFo+Oabb3BxcWHx4sV4eHjw0Uc25SbGGZ0ZY5xOawELIc4CzYCbgIuU8pHRepIPtU5myXHu3Dm8vb25cOECoaGh2OW85CtmTk7aK9ScHB21dw2NzcIi/4stISSVK8cSHz+atLSN2Nra8tlnnzFhwgQqVapk/M6UArt27cLb25vLly/zxhtvsHjxYlxdXfVvMDVV++gjRzHdKVuaE3nPDocaCfh6nM11J4WePcHKSq0FrJifLGAtYCsrDp45g39wMOt/+AELS0scHDREReW+L6NLjHv06BFz587Fz8+P5ORkKlasyNSpUxkzZkyui7CCY5z2Ea6v75NETqPR8O9//5ujR4/y0ksvsWzZMtzdS/yfWMll5hhnpeP+f6FNACeZIvlTSobbt28zffp0vvrqK6pWrcr06dOxstL1V8dwvr7axxNZr1iL8zGDg0PeCSeAlIKHD6sBK2nXrg1bt/bAoZwPeNm3bx+2trbs2bOHLl26GN5gVI7xUUH18VrjTkKy9nfv2t2KeK3Rxr5sATIqCgqYda4o+UpN1f7+REdDcrJ2Nm/dutoxffrEPCGgeXNo2vRJuykpXL17l8/WrmX7nj04OjoSGRWFk5MT8+ZZ6B3jMlbTmTBhAjdu3ACgf//+zJs3L9+nMwXHuCePhB89esjQoVWwsLCgT58+DB8+vFyuWGR0Zo5xRb4DmF72JZT0cjBSl1uHBlBXx+YVHh5O69atSUxMZPjw4cyYMYPq1aubrT8BAdrHE5GRua9Oi+NcOYNxXorrDmRJFx0dzeTJkxk0aBAvv/wy8fHx2NjYGO/i4OhR7X+Y6ZyGd+Pa3dzlYhzt47m68pcnG+rWhfbt1R1ApegKulOX8QjX2Vn7WNeApOfx48f4+vry+eefY2lpyaRJk/D29uapp57K3EefGHf8+HFGjRrF8ePHAWjTpg3Lli2jbdu2BR5X1BgnRCQ//3yObt26Fen7VIrIzDFOl0j9GdAA8DRV8qeYh5SSixcv0qRJExo1asSIESMYOHAgjRs3NnfX8PQ03biSjPOMG5fMjS0WzdMAACAASURBVBtWgEh/ZVeeyrkAJCQksGjRIhYsWEBqaipt27bl5ZdfNn4tv+TkbB8j7+U95CDX9pQU4/ZDKdsyBuHnNw4rY1t4ODx4AO3b65wESikRQmBhYUFgYCB9+vRhwYIF1KtXL9e+usS4GzduMGnSJDZs2ABA7dq1mT9/Ph9++CEWFoUP8c84T0bCmd//7FLWo2lTVe3N6Mwc4wr8DRFCVBdCeAgh5gGzAT8p5Z/6nEgIEZfjlSaE+EKftpTi87///Y8OHTrg7u7OzZs3EUIwb968EpH8mdq9e/f488+R3L5tB1giRFSe+5WnJ7/btm3DxcWFGTNm0L17d0JDQxk6dGjxnMzIxXRNQcW5UqiYB+GfOHGCvn37Eh8fj7W1NcHBwQQEBOSZ/BVVUlIS8+fPx8XFhQ0bNmBtbc3EiRMJCwtjwIABRUr+MmStwODomPc+jo4WBRbzV/Rk5hhX2G9JF+Bb4L/AEmCCvieSUlbKeAG1gURgm77tKcYVFRXFBx98QJs2bbh06RJLly6lZs2aufbTpS5eaZV1JuuKFSuQUuLl5cWKFVXLfamDyMhI6tSpQ1BQEIGBgcX7n4IBxXTNRcW5UiY19Un5jXQBQfVxGt4Ni37v4DS8GwFBWWr6ZZTjKEJh3lu3bjFo0CBefPFFgoKCuHDhAgCVK1fWu7tSSnbt2oWbmxuTJk0iLi6OXr16cf78eebNm2dQ2/fu3WP27LRyH+NMyswxrsAEUEq5RUoppJTPSCnHGXHFjz7AbSDISO0pBrh16xZNmjTh+++/Z/LkyYSHhzNo0CAsc5Qv0KUuXmkkpWTnzp24ubnh7e1NbGwsr7/+OqdPn2bNmjUMH1613JU6iIiI4J133mHTpk0AmeOMXn755eI/uZ7FdHMeZ0YqzpmSPqso5DMI/9rdikgpMgfhZ0sC8zguq7S0NBYtWoSzszMbN27E29ubsLAwg2fLhoSE0KVLF3r37s3ly5dp2rQpe/fu5YcffqBRo0Z6t5uamsoXX3yBs7Mzjx6tKXcxzqzMHON0KgNjLEKIA8DvUkqfwvZVA6SLR1paGn/88QcdOnQAYNWqVXTr1g3H/J4BYHgZFlNO4NDVqVOnGDt2LIcOHQKgcePGLF68mG7dupXLmW4PHjxgzpw5LF++HCsrKxYuXMiIESNM35GzZ3PdocmXpaV2oH7z5gBmnwRS1DinYpyBDJnAYeAg/JwCAmDyZElkpMTW9g5z5mjw9q6j97cGcP/+fXx8fPjyyy9JS0vj6aefZubMmQwbNkyvovtZ7d+/n9GjR3P+/Hk6derEsmXLaNasmUFtKjoyY4wzeS0PIYQj0BH4uIB9vAAvoNyX1igOBw8eZOzYsfz999+cO3cOV1dXhg0bVuhxhqy3m3O2WcbdQzBvEhgdHc2UKVPYsGEDUkqqV6+Oj48PQ4cONTi4llbfffcdn3zyCffu3WPAgAH4+voWe5HvfDVrph14X9gYrfRiupSQ/7wKi3Mqxhkga6mWpCSIi9MOitdocu9b2AQOIw3Cv3jxIh988Avnz48iMdECEDx+/AzTp0Pt2vrFuLS0NL766iumTp3KvXv3sLCwYNiwYcyaNQt7e3vdG8xh/PjxfP755zRo0ICdO3fSq1evcnmxa3ZmjHG6rARiLB8CR6SUV/LbQUq5VkrpLqV0z2scmqKfsLAwevXqRadOnYiJieHbb7+ladOmRT7ekPUe9ak4r4+ijlFMSEhg9uzZuLi4sH79/7N35mFRlV8c/1xAUHBLwV3RZHEBc0HLXSst06zUMkUrzTR3AXdIQ0VyARW1TC3LMM31Z4tLaZZ7ipmKKUsuuCvuIArMvL8/hkGWQYaZYYbB9/M89xnnzr3vPVyZw7nve873fIOdnR1+fn7Ex8czcuTIpzL40/ZwLlGiBA0bNiQqKooVK1ZYLvgDzR/r1q0ft9XK2VXBzu7xU7EB1ZmFyBP9nPRxBqCrX+rNm5ogUFfwl5W8CjiMTMK/e/cuAQEBeHl5ceRIz4zg7zGG+rjdu3fTrFkzhg4dys2bN2nfvj1///03n3/+Odu3Oxuch52UlERSUhIAXbp0ISQkhH///Zc333xTBn+WwpI+Tghh1g2IBQbqe3yzZs2ExHju378vSpX6UCjKeQFqUbOmSkRGFmyMyEghHB2F0HhizeboKPQaR1Gyn6fdFMWwn8dQ+1Qqlfjuu+9EjRo1BCAA0aNHDxEXF2c6Q6yM6Oho8eqrr4rg4GAhhBBqtVqo1WoLW6WDtDQhzpwRYu9eIXbt0ryeOaPZrwMgSpjZv2m3gvg56eP0QK0WYs8eITZsEGLt2jy3yJEHhKtzklAUtXB1ThKRIw9kP2bDhuy/L2fOZBszcuQB4Wiflt2H2KdlH2fDBiHOnBFff/21qFSpklAURQwaNEgoitpoH3fu3Dnx9ttvZ/omV1dXsW7duszvo6E+WKVSiZUrV4qqVauKsWPHGvAfIDELZvZxZp0BVBSlFVAdWRVnFtLS0li3bl1G5Vhp1OolCFELULhwwabABRzG9LQ1ZvZQX540y7hqFVSp8hBbW+jfvw0XL7aladOm/PHHH2zYsMGoJGpr5caNGwwdOpRGjRpx8ODBzKpvRVGK5myAnZ1G/b51a+jQQfNap45hHRoKEennCgE9pFoMKuAwIgl/z549uLu7c/jwYZYtW0atWrq/M/r4uOTkZKZOnUq9evVYt24dpUqVIjg4mFOnTtGrV6/M72N+Pk7XzOChQ4do3bo17733HjVq1KBHjx75GySxDGb2cWYtAlEU5UvAUQjRX99zrCpB2tRthAxk1SqBn18KN26UBBL45JOHrFxZz6x9dHPbpLuNmykrzPLuaymwtX2ESlUyc4+9fTpffWVDv36WyIKwPOvWrWPQoEEkJyczdOhQpk6dapK8oqKEpYpACurnrMrHgfn9nJ79UgNXextWwKFnEv6FxEQmfP89/sOH4/Puuzx48IBSpUplBmeG+DghBD/88APjxo3j4sWLALz77rvMnj2bmjoqPZ/Uu9fRMfe1e/bcxnffdSmwQLTEOrCqIhAhxBBzXs9sPKkK7do1Tb6KCdoI6UNoaAJBQZVQq7VJy7UJCxN5tvoxVxeLnIrzhVEFnHdfS1W24A8gNdWOoCDo18901y/qCCFISUnB0dGRZ599lrZt2zJnzpwC5YFK8kf6ORP7OT37pT5ItdV1dv5dFPJJwk9JTWXOjz/y2f/+hwA6DxyID+CYQzCvoD7u77//ZtSoUezbtw+AJk2aEBER8USJpbx8nK2t7pnBnTtfYvz48QQFBRmlESgpnlhEBqYgFPmnY5FPGyEt2gqeQkxUT0tLo1Spa6hUuRXmbW11m1ec+tjq7muZDJRCV72TouSfO15ciIqKws/PDzc3N1asWGFpc8yCpWVg9KXI+ziwrJ/TU6rF1kaNSp37e66XhEsewe3/Dh1i9DffkJCYyNudOzN7yRJqGyl+fv36dQIDA/nqq68QQuDi4sLMmTMZMGBALu3VnOQ1y5jXA36R9XFFZLXM2jHWx8m5YGMp5DZC+ZGSksLChQtJTU2lRIkSqNXV87x8cVd49/WFESOOUaLEZUANnMPDYy5Vqujum/g0qG9cvHiR9957j+bNmxMbG0trHdplEkm+WNLP6SnVolIrhndRUBSNtlr37tCkieYYFxdO3LlDeWdndu3Ywdrt240K/rQdhtzd3Vm+fDm2trYEBATkKbyvi7zysKtWTdV5fJHzcboquRMTNa9Hj2r2nziR9zq3xKTIANAYCrGNUH4IIVizZg316tVj1KhRbN26FSDPRGStoyiuCu///vsvXbp0YfbsxqSlVadOHTfWrYvi9OkpzJ3rUOyDX11s3rwZDw8P1q5dy8SJEzP/0EgkBcKCfg7QW6rF1fmB0V0UEu/cYejs2Wy8dg06dGDC4sX8HR1Nh5deMupH2Lp1K40aNSIgIIB79+7RpUsXoqOjmTt3LuXKlSvQWFl79547ByVLbuDq1Q+B7PelyPk47Syy9ncp58OEdl9cnOY4GQQWOjIANIZCaCOkDwcPHqRVq1b06dOHChUq8Pvvv/PGG28Ami98XsFOTsdRHIK/xMREhg8fTqNGjdi2bRtly5Zl1qxZ/Pvvv5nVc8ZUL1sbarWaxMREAHx8fOjVqxenT58mNDSUsmXLWtg6iVViIT+XSQH6pfq2vcC5z7eg/mE95z7fkj3402qp6VhiTEtLIyIiAnd3d5YtW0ZMTAwA9vb2es3M5UVsbCxdu3bltddeIyYmBnd3d3755Re2bNmCp6enweOqVCouZyyLd+jQgZEjK/DFF+lF28dZeLVMkhsZABrD5cvZfpkDV3tnJiZreZBqR+Bq78c7VKps+SyGMGLECM6dO8fXX39NVFQUHTt2zPzsaQl2Hj16RFhYGG5ubnz++ecIIRg6dCjx8fGMHz+ekiWzF31YW/Crr6B1Vv7880+aN29Or169EEJQvXp1Vq5cSe3atQvZWkmxxkJ+LhNDpVqy8oQuCn/++SeNGzdm9OjR+Pj4cOzYMSZNmmSUyXfv3mXs2LF4eXmxZcsWypYtS1hYGNHR0bz22mtGjf3nn3/SrFkzXn/9ddRqNRUrVmTBggV8/HHZouvjLD2LLNGJzLY0BhO1EcqP+/fvEx4ezsiRI6lQoQJr1qyhSpUqlC5dWufxvr5F7MtvQoQQbNy4kfHjx3PmzBkAXnnlFcLCwmjYsKGFrTMNBW2bpw16N23aRM2aNQkICDCfsZLij5n8XJ7Y2Wlm7rIEEL5tLzw54Mt6rhBPrE6+ePEijx49YvPmzbz++utGaWCq1WpWrFjB5MmTuX79OoqiMGjQIGbMmEHlypUNHhfg/PnzjBs3jnXr1lGrVi3mzp1bNPU6daFnJTeQ/f/1wgWNDp6kUJABoDHoyE3RVZ2WVxuh/FCpVKxYsYKgoCCuXbvGs88+S//+/Z9K0WLQVLL6+/uzZ88eABo0aEBYWBivvvqqhS0zLU8Se80ZAP76669069YNe3t7ZsyYgb+/P6VKlTKfsZLiTyH7Ob3Qt1+qomjsLVMmz8rS+/fvM3PmTKpWrcqoUaPo27cvvXr1wsHBwSgT9+/fz6hRozhy5AgArVu3ZsGCBTRr1syocbVjv/TSSyiKwqeffsq4ceNyydAUaQowi5wZAGpnkWUAWGjIJWBjKEBuSiZ5VaHlYOfOnTRt2pSPPvoINzc3/vrrL/r311s/u1hx8eJF3n//fZo3b86ePXtwdnZm8eLFHDt2rNgFf5C3NqN2f1paWubsZ+vWrRkxYgRxcXEEBgbK4E9iegrRz+mNvv1SPT3h9dehY8dcXRTUajUrV67E09OTzz77jLgMyRdFUYwK/i5evIivry+tW7fmyJEjVK9ene+//549e/YYFfwJIUjI+NL7+Pjw8ccfc/r0aaZOnWpdwR9YfhZZohMZABqDEW2E8mPRokXcu3ePH374gT179tCiRQtTWm4VJCcn8+mnn+Lp6cnKlSuxt7dn3LhxxMXFMWTIEObNm4cqv4RiKyQv6YaaNQW//PILjRo14pVXXiE1NRUnJyfCw8OpWrWqeY2UPD0Uop8rEHlItVCtGjRurNnv7a1zmffo0aO0bNmS999/n5o1a3Lw4EEWLlxolDkpKSnMmDEDT09Pvv/+exwcHAgKCiImJoY+ffoYtTx79OhR2rdvT8uWLUlKSsLe3p558+ZRq8jpuuiJnpXchTqLLMmFXAI2hoLmpigK1K2rswrt5s2bTJs2jWHDhuHp6cnSpUspU6ZMrmKGpwG1Ws13333H5MmTMyvdevbsyaxZs6hbty4AEydOZNasWdy6dYvQ0FBLmmtyQkJyi72WLKmmbNlZdOs2GXd3d8LCwighnaPEHJjQz5nMnjp1CrQ0+ODBAy5evMi3335Lv379jGqHps1DHjt2LOcyVPR79uzJ3LlzjS64un79OkFBQSxfvhxnZ2dmzpxZPGb1q1XTdIvJ+P0J6XMiWw4gmGEWWZILGQAai765KaBJRo6P15R2ZiQkp6amsnjxYqZNm8a9e/do2LAhnp6euLi4mMf+Isbu3bvx8/Pj77//BjRLH+Hh4bRt2zbzmNTU1MxuFitWrCA4OBj7HE+Y1kzOllKVKz/i6tWBXLq0lfnz5zN06NBi9fNKrAAj/Zy5efjwIfPnz+f27dvMmjWL1q1bc+bMGaPz/I4fP86YMWPYtWsXAN7e3ixYsCCbEoOhnDlzhqZNm5KcnMyYMWOYMmUK5cuXN3rcIkHNmhqh5wy0Dw85+zkX+iyyJBsyADQWbW6Kto2QWv1kAUu1GuLiEHfu8NOtWwSMHUt8fDydO3cmLCwMLx0yBU8D8fHxTJgwgY0bNwJQvXp1Zs6cqfNpfcWKFVy/fh2Aa9eu8e233/LRRx+Z3ebCpGfPh3h4nKB58+ao1SWYO/c5Bg1aSIUKFSxtmuRpxEA/x927hdr+MidCCH788Uf8/f05c+YMPXr0QK1WY2NjY1Twd/PmTaZMmcKSJUtQq9VUqFCB6dOnM3jwYOyMnOk8d+4ctWvXpk6dOgwfPpx+/foVv/7cBZ1FfoJmo8R0yBxAU6DNTclYnswPkZ4O16/zx4YNlChRgi1btrBt27anMvi7c+cOY8eOpUGDBmzcuBFHR0eCg4OJiYnhvffeyxX8paamEhISkin50qBBA0JCQkhN1d0KydoQQvDDDz9Qr149OnfuzL1797CxsWH8+PEy+JNYlgL6OVQqzbKfmQR9//vvPzp37sybb76Jg4MDv/76Kxs2bDBquTc9PZ2FCxfi7u7O559/jqIojBw5kri4OIYNG2ZU8BcXF0e3bt1o2LAhFy9eRFEUQkJCil/wp8XLS6PFmJ+w9hM0GyWmRQaApiI9Hf77L9tTcU6hy8XbKvDhF1+w6+RJFLWaGV26cPzvv+nSpYv16DmZiPT0dBYvXoybmxthYWGkp6fzwQcfEBsby5QpU3Byyi0zAXD58mVu3rzJwIEDARg4cCA3btzgypUr5jS/UDh06BBt2rTh3XffpXz58qxfv15275AULdLTNcu7T/Bz2QR91WqIjTWLoK+NjQ3R0dEsWLCAY8eO0alTJ6PG27FjB8899xyjRo3i9u3bvPzyyxw7doyIiAijHsbu3bvH+PHjadiwIbt37yY4OJhKlSoZZatVoG8lt7u7WWeNn2bk/Kqp0EPocsTXL2CjVKNx7Qu86OWFo4MDXLnyVOkcCSHYunUrY8eO5dSpUwC0a9eOefPm0bRp03zPr127Nrdu3eLAgQMANGvWjJs3b1p9sUxMTAzPP/88lStXZvny5XzwwQdGtaCSSAqFCxc0QV0Gegn6qtWFIuirUqlYtmwZ+/fvZ+XKldSpU4fz588bnR/733//ERAQwObNmwF49tlnCQ8Pp3v37kY/qN+7d4969epx9epVPvjgA2bOnEmVKlWMGtOq0M4i16+v+Z24fFkj9VKihE7NRknhIu+0qdBD6BKcKFtqKSO7/KJ5+5QJXUZHRxMQEMCvv/4KQN26dZk9ezZvvfVWgRxrzlweaw3+kpKS+OOPP+jWrRuenp5ERkbSvXt3ypQpY2nTJBLdXLyY7a1egr4Ax49r+pPlIc5cUP744w9Gjx7N8ePHad++PUlJSZQuXdqo4C8pKYmZM2cSFhaWKbEUFBSEn5+f0cUjZ86c4dlnn6Vs2bL4+/vToUMHfHx8jBrTqjGgkltiesy+BKwoyruKopxSFCVZUZT/FEVpm/9ZVoCeQpd3HpTKvkTyFAhdXr9+nY8//pjnnnuOX3/9lXLlyjF37lxOnjxJjx49nrrlb5VKxVdffYW7uztvvfVW5vK1r6+vDP6KCcXWzyUlZXt7PlG3n8u1PzUVEhM1D7xHj8KPP8KJE08uJNHBtWvXePvtt+nYsSN3795l3bp17Nq1K8+2mPqglZ3y9PQkNDSU1NRU3nvvPWJjY5k4caJRwd+lS5fo168f7u7uREVFATB27NinO/iTFBnMGgAqitIJmAUMAMoA7YAz5rSh0Mh48jx7/Tq9582jgtOtPA5UsjdNL8Zabg8fPmTWrFm4ubnx5ZdfoigKI0aMID4+noCAAKOfqq2RXbt24ePjw6BBg6hduzZ79+6VIs7FjGLt53I8sNra6A7g8toPaFY+VCpNRei+fQUKAkuWLElUVBTTpk3j1KlT9OrVy6gHyMOHD9O6dWvee+89Ll++TPPmzTlw4ADffvst1YzQoEtJSSEkJAQPDw/Wr1/PpEmTqFevnsHjSSSFgblnAIOBaUKIg0IItRDikhDikpltKBTulSnDxO+/p76fHz8dOUKXJusB3Y4tc3bQTEKXq1ZB7doaWa7atTXvCxMhBOvWraNBgwZMnDiR+/fv89prr3HixAkWLlyIs7Nz4RpQRLl69SqvvPIKt2/fZs2aNezfv5/nn3/e0mZJTE+x9XM5H1hVat3BV177sx+k0ugKPqFKWAjB6tWr6dKlC+np6ZQrV47Y2Fg++eSTbALJBfVxV65cYcCAAbRo0YKDBw9SpUoVvvnmGw4ePMgLL7yQv+1P/LFUtGjRgqCgILp06cKpU6eYMWOGUbOUEklhYLYAUFEUW8AHcFEUJV5RlIuKoixSFCWXzLmiKIMVRYlSFCXqxo0b5jLRYNavX4/byy8z63//o3fLlsRFRPDdyApULKNbmiRru5tVe2sVanC2apWmq8T585oH7fPnNe8LKwg8dOgQbdu25Z133uHs2bM0bNiQ7du388svvxRY3uD06dMMGzYMd3d3nJycKFu2LPXq1aN3795WI/ty69Ytli5dCkCVKlXYtm0bp06donfv3k/d0vfTgL5+ztp8XCY5ghhXZ90tvbLuf2KVsHYmUEeV8JEjR2jbti19+/bl+vXrXLt2DSBXB5yC+LhHjx4xa9YsPDw8+Oabb7C3t2f8+PHExsby/vvvGyUZEx8fjxACW1tb/Pz82LlzJ+vXr6eOzHOTFFWEEGbZgGpopsSigKqAM7APCHnSec2aNRM6SUsT4swZIfbuFeL33zWvZ85o9puJ9PR0IYQQP/30k2jXrp2IWrNGiA0bhFi7Voi1a0XkyAPC0T5NaNySZnO0TxORIw8IsWGDiAw9LxwdRfbPHYWIjDSdja6u2cfXbq6upruGEEIkJCQIX19fkfF/LCpVqiS+/PJLkWbg/8euXbtEyZIlhYODg+jZs6eYOHGiGDlypHj11VeFp6en2LVrlwDErl27TPuDmIjU1FQxf/588cwzzwgbGxtx6tQpS5v01AFECTP5N+1miJ/L08cJUST8XDbOnBFi3Tr9fJwen4u1azU+88yZzEvcu3dPfPjhh0JRFOHi4iKWL1+e6Wt1oY+PU6vVYvPmzaJu3bqZPqp79+4iLi7O6FuSmJgohg8fLmxsbMT69euNHk8i0RdjfZw5q4BTMl4XCiGuACiKEg4EAYF6jyLEYzV6yN6W6No1TYKxu3uhtiA6deoUY8eOpWnTpkyfPp2uXbvStWtXFNDktGS0S8qz3U2Hy1CpEoF+NbP1ewVN/9fAwMftwIwlIaFg+wtKUlISs2fPZu7cuaSkpODg4ICfnx+TJk0ySsMuMDCQtLQ0Dh06pFMe5o8//jDC6sJDCMFPP/3EuHHjiI2N5eWXXyY8PFzm/zw9FBs/pxNtS68s3Rwg75ZeelUJ51BDKFWqFEePHsXf359PPvmEcuXKPdGk/Hzcv//+i5+fX6b6QP369Zk/fz6dO3cu8I+flfT0dL788kumTJnCnTt3GDp0KB06dDBqTInEnJgtABRC3FYU5SLZE+MKVgImRLYAKxfafYXUgigxMZFPP/2UJUuW4OTkxKuvvgqQfSkva7skcrS7sbPT/AwZjjvhgm7bTBWcAdSqpVkS0bXfGFQqFStXriQwMDCzivWdd95h1qxZRjdEB829LleuHA0aNDB6LHOSlJTEwIEDcXZ25qefftI8GMil3qeG4uDnnkgBW3rlpYaQc//WvXsJnTSJn376iXLlynHw4MFcS715kZePq15dxejR/ixevBiVSkX58uX59NNPGTZsmN5jP4nXX3+dbdu28eKLLzJ//ny8vb3zP0kiKUKYuwhkBTBSUZRKiqI8A/gBP+t9dnS0fs3I9UguLigbN27Ezc2NJUuWMGTIEOLj4xk5cmTuA7VCl927Q5MmmiIPFxfNa+PGmv3e3qAoeQZhxgZnWQkJAcccPtjRUbPfULSVrAMHDuTKlSu0aNGCffv28cMPP5gk+AMIDw/Hzs6Opk2bEhAQwKeffsru3btNMrapuXr1KlOnTkWlUlGmTBl27drFiRMn6Natmwz+nk6s1s/phZeXxqfpQdZ8Z137Yy9fpmtoKK9NnMi1a9e4kCGoX5AATZePs7dP4/btoURERCCE4OOPPyY2NpbRo0cbFfydPXs2M/946NChbNy4kR07dsjgT2KVmDsAnA4cBmKBU8BRQL9QJD0921MnGJ5crC9CCB5krNG6ubnRpk0bjh8/zuLFi3HJzwFqhS5bt4YOHTSvdepkEz8tjOAsJ76+sHQpuLpqYlNXV817Q5aY4+LiePPNN3nxxRf5559/qFGjBpGRkRw4cIBWrVqZzGYhBNeuXcPV1ZVTp04RHh5OcHAwly9fNtk1TEFKSgozZ87E3d2d0NBQDh8+DIC3t7dJZhgkVotV+TmDUBS9Zh1D+pzA0T67bY726UzvfYyxK1fSMCCAvTExzJ08mRMnThjUDz27jxOUKHGZ1NT3SU5eRvv27Tly5AhffPFF/j77CSQlJREYGEj9+vVZuHAhAN27dy+wiL1EUpQwawAohEgTQgwTQpQXQlQRQowSQjzU6+Q8Wq2dT3RCCCWzBVE256jjPH05cuQIHTp0YPDgwQA0atSIn3/+2aRLkqYMzvK7zrlzmo5M584VfPzbt2/j5+dHgwYN2Lx5M05OTkyfPp2YmBh8fX2NqpzTeVXcjgAAIABJREFUxahRo/jwww/x8fEhOjqahw8fIoTg3XffNel1DEVkSFPUq1ePwMBAOnXqxMmTJ42Wj5AUD6zJzxmEdoZSD/0+37YXWDokClfnZBRF4OqczNIhUfRvf4mYy5d5v317YhcuJCA42KguHm3bJvD8870Rwoa0tOrUqrWPtWvXsmvXLho3bmzwuGq1msjISDw9PZk5cyZvv/12kfFDEomxmL0TiMHo0WpNm1yciTa5uABcunSJDz74gObNm3Pq1CnatjVOwD8/fSpjg7PCJC0tjYULF+Lm5sb8+fNRqVQMGDCA2NhYgoKCcMw5fWkCrl+/zueff84rr7zC559/TsOGDYucYLRKpWL69OlUrFiRXbt2sXHjRtzd3S1tlqQ4YCY/ZzAFnaFEEwSe+3wLe4JnUL1iM1p6aDpibBo3juXDh1O5eXOD28I9ePCAqVOn4unpydq1aylVqhTBwcGcPn2at99+2+jZuaFDh9K/f3+qV6/O/v37+e6776hevbpRY0okRQXr6QWsZ6u1XPsL0Grt559/pnfv3qSnpzNu3DgmT56cbwXak9DqU2krfbX6VFC0Ar2cCCHYsmULY8eO5fTp0wB07NiR8PBwo56m9eH69euo1Wru3buHSqXC1tY22+cpKSnZBGDNxblz5wgNDWXu3LmUKVOGX3/9lapVq+ayTyIxCjP4OaPIY4ZSG6RqZyjhcYXwxZs3GR8Zyep9+6heoQIXb97k2cqVsbO3h0qVNDmFBUQIwQ8//MD48eMz8wZ79+7N7NmzqWVkEvXVq1ext7enQoUKDBgwgBdeeMFojUCJpChiPb/ROZYH8ksuziSfXCy1Wo1WiNXHx4eePXty+vRpZs2aZVTwBxo5l7xkXooqx48fp3PnznTr1o3Tp0/j7u7O5s2b2blzZ6EHfwCenp54eHhw4MABGjRowIgRIwgMDGTIkCG0adOG4cOHF7oNWbl3715mG6fvvvuOv/76C4AaNWrI4E9iegrJz5mMAs5Qhm7ahOeYMWw8dIigHj2ImT+fdo0aabogubsbVMF89OhR2rVrR58+fbhw4QJNmjRh9+7drFmzxqjg79GjR8yePRsPDw+mTJkCwAsvvMCAAQNk8CcplljPb3W1ahqnkUFeycUhfU483pFPq7U9e/bQokUL3nzzTdRqNZUrV2blypUmU24vbA0+U3L16lU++ugjmjRpwo4dOyhfvjzz5s0jOjqa7t27my3RuUSJEuzcuZOPPvqI1NRUli5dyvz589mxYwdVq1ZlwIABZrFDCMHSpUtxd3fns88+4+233yYmJoaXX37ZLNeXPKUUgp8zKQWcobx06xavNWnC6UWLmD5yJE7PPptLDUFfrl+/zuDBg2nWrBl79+7FxcWFZcuWcfjwYaNSdbTanV5eXkyYMIH27dszevRog8eTSKwF61kC1gqQZpCfAGm283Lw33//MWHCBDZs2ECNGjUIDQ1FURSTBzmFpcFnSlJSUpg3bx6hoaEkJSVhZ2fH8OHDmTp1KhUrVrSITTVq1Mhsn2ZJ1q9fj7u7Oz///DPNmze3tDmSpwET+rlCQccM5flEp1yHVSp7F4AFAwZga2OjCVBbtzbokqmpqSxevJjg4GDu3r2LnZ0dI0eOZMqUKZQvX96gMbMSGhpKYGAgnp6ebN26NVPfVSIp7lhPAFhAAdLMJYYcycW///47Xbp0wc7OjmnTphEQEFAoxQygkXPJmgMIppd5MRRtDs2ECRNIyJiS7NatG3PnzsXT09PC1lmG06dPM3nyZObNm4erqyvr1q2jbNmyUuZBYj5M5OcKjWrVNJ1IMmwL6XMiWw6ghge82/oXwF4T/BkxQ7lt2zbGjBlDTEwMAK+++irz5s0zurPOnTt3SEpKokaNGvTp04dSpUoxYsQIKd8keaqwniVg0CQLV6qUbYlEJ7a22ZKL09PTiY+PB6Bly5aMGDGCuLg4Pvnkk0IL/sB8Mi8F5eDBg7Rq1Yo+ffqQkJCAt7c3v/32Gz/99NNTGfzdvHmTkSNH4uXlxY4dOzhxQrO8Vq5cORn8ScyPgX7OLNTMXeHbt+1KbJQEQE2Zktf48qNDzP/A/onn5UdsbCzdunWjS5cuxMTEZM7Eb9261ajgT6VSZaZ2fPzxxwDUqVMHPz8/GfxJnjqsKwBUFM0ygru7xvnldJB2drmSi7du3UqjRo3o1KkTjx49olSpUoSFhVHNTDkzRUnm5fz58/Tt25eWLVty8OBBKleuzLJlyzh69OhTm9sWERGBm5sbn3/+OYMGDSI+Pp5u3bpZ2izJ04wBfs5saGcobW0RGTqA9ar9wYte3Tgxdx73Vu5mcKcbj48v4AzlvXv3GDduHF5eXvzyyy+UKVOGOXPmEB0dTdeuXY0yfffu3fj4+DBkyBDq16/PjBkzjBpPIrF2rGcJWIu21Vr9+hpJgsuXNRIIJUpolhlq1gQ7O6Kjoxk7dizbt2/Hzc2NefPmGSU0as3cv3+fzz77jPDwcB4+fIiDgwMBAQFMnDiRMmXKWNo8i3Ly5Emef/55wsLCaNiwoaXNkUg06OnnLMF/pUoRMG8ebzdrhm/r1vh17Yq/rraHBZihVKvVfPPNN0yaNInr16+jKAoDBw5k5syZVK5c2Wibv/vuO9577z1q1qzJDz/8YBKNQInE2rG+AFCLttWajordqKgonn/+ecqUKUNYWBgjRox4KoM/lUrFihUrCAoK4tq1awC8++67fPbZZ7i6ulrYOsNIT0/P1hNYrVbz+++/0759e71kWY4ePUpAQAAhISG0bNmShQsXPpW/GxIr4Ql+ztzcv3+f0NBQwsLCKFGiBK926AC2ttjY2mbvW2xnp+kS4u6uCf7yCbQOHDjAqFGjiIrSCES3atWKBQsW4OPjY5S9Dx484NKlS7i7u9O9e3dmzpzJ6NGjCzXtRyKxKoQQRXpr1qyZ0IeHDx+Kv/76SwghhFqtFrNnzxY3btzQ69ziyI4dO0SjRo0EIADxwgsviP3791vaLKOJj48XgBgzZowAxOjRowUgzpw588TzLl26JD744AOhKIpwdnYWGzduNJPFEksBRIki4MPy2/T1cZZk06ZNomrVqgIQ/fv3F5cuXdJ8kJYmxJkzQuzdK8SuXZrXM2c0+/Ph4sWLwtfXN9NHVatWTURGRgq1Wm2UrWq1WqxZs0bUrFlTNGzYUKhUKqPGk0iKKsb6OIs7v/y2/JyjWq0W69atE3Xq1BFlypQRt2/fLug9LFacPn1avP7665lOtVatWmL16tVGO9WiglqtFq1btxYuLi4CEM7OzqJNmzZP/PnCwsKEk5OTKFGihBg7duxT/zvytCADQOPRfq82bNggWrRoIQ4cOGD0mCkpKWLGjBnC0dFRAMLBwUEEBgaK+/fvGz3233//Ldq2bSsA0bhxY7F7926jx5RIiipPdQB46NAh0aZNGwEIb29v8euvvxpyD81KZKQQrq5CKIrmNTLSNOMmJiaKUaNGCTs7OwGI0qVLi5kzZ4oHDx6Y5gJFiN9++y0zwAXEjh07ch2jUqky/3iFhoaKnj17ivj4eHObKrEgMgA0nMuXL4v3339fzJgxQwihCQT1nUnLy8ep1WqxYcMGUbt27czvbo8ePfKdvc+XjFnIXRERmhn+8uXFlyEhIv3hwwKPIfbuFeL33ws0kymRWIqnNgCMj48XiqKISpUqiaVLl4r09HRD76HZiIwUwtFRc9e1m6OjcUHgo0ePxLx588QzzzwjAKEoihg0aJC4cuWK6QwvYqjVauHl5ZUZ+Oec/du7d69o3ry5WL16debxkqcPGQAWnIcPH4rQ0FBRunRpYW9vL4KDgwt0fl4+LjT0vOjYsWNm4Ofl5SV27txpnLFqtUg9ckScmDdPiA0bRNrq1WJmnz7i9ooVQmzYoNmOHxfiSd9/tVpzjPb4tWsfb/qOIZFYCGN9nFXJwCQlJfHjjz8CULduXVatWkVcXBwfffSRVfRlNWVvYCEEmzdvxsvLCz8/P27fvs1LL73E0aNHWbZsGVWqVDGN0UUQRVGYMGECAOPHj8+s5jt79izvvPMObdq04fLlyzg4OGQeL5FInszu3btp2LAhkyZN4sUXX+TkyZOZPXH1JS8fN2mSYNeuXVSoUIHFixdz9OhRXnzxRcONFYLt4eE898YbvDh1KknJydjZ2jLprbco7+SkKUpRqTSC2vv2aWJRHWOwb99j0e2shSyg3xgSiRVj1gBQUZQ/FEV5qChKUsYWo895WokADw8PevTowYULGlX8Pn36ULZs2UK12ZSYqjfwP//8w8svv8ybb75JXFwcnp6e/Pjjj/z2228899xzxhtqBfTr14+UlBT69esHQHh4OPXq1eOXX37h008/JSYmhrfeesvCVkqeRgz1c5ZCZAQ2Tk5OlCxZku3bt7N582bc3NwKPFbevqwmw4cPJy4ujmHDhmFnhIRNXFwc3Tt25NWxY0lLT+erjz/GKeNhLxcqFVy/DtHRuT+LjtZ8ljPwK8gYEokVY4kZwBFCiNIZW75tJ+7fv4+Pjw8DBgygVq1a7Nmzh5rm6ntpYvLqAaxvb+ArV67w4Ycf0rRpU37//XcqVKhAREQEJ06c4PXXX9c507VqFdSuDTY2mtdVqwputynGKAzs7OxIzWhOX7NmTfr06UNsbCxTp07FySl3f1KJxIwUyM9Zgtu3bzN69OjMjhjNmjXj+PHjdO7c2eAx8/JlVaums2jRIipUqGDw2KDpENKwYUN2/fUXs3x9iQ4L43UfH77fW4vaw17Dpncvag97jVV7svyN0M7ipac/3peenq3dHsCqPTULNoZEYuUU+SXguLg4EhMT+f777zlw4AAtW7a0tEkGExKi6QWcFX16A6ekpDBjxgzc3d35+uuvsbW1xc/Pj/j4eEaOHJlnC6NVqzS9iM+f16xenD+veV+QAM4UYxQG27dvp3HjxoSFhQHw9ttv880331C9enXLGiaRFHFUKhVffvklHh4eLFq0CFtbW9RqNQA2Nob/SThz5gzOzuFAcrb9jo6COXMM19pUq9X8/fffAHh4eDBr/HjiFi1i/Btv4FCiBKv21GTwlz6cT3RCCIXziU4M/tInewAHGkFtXf8Gw8aQSKwdYxIIC7oBfwA3gERgH9Ahv3M8PDyKVSVrQaqAVSqVWLVqlahZs2Zm8vQbb7whYmNj9bqWq2v2ZGzt5uqqv72mGMOUnDx5UnTp0kUAom7duuLHH3+0jCGSIg8WKgIpqJ8zZxHI8ePHxXPPPScA0b59e/HPP/8YPeb9+/fF5MmThYODgwCEvf0Honz5O0JR1AVXOshRjbt/yRLh4+0t7O3txfnz5zXH7N2brVjD1TlJt49yTspe1LF37+PrmGIMicTCGOvjzN0JZALwL5AKvAv8pChKYyHEf1kPUhRlMDAYoFatWpQqVcrMZhYevr769QPev38/fn5+HDp0CIDGjRsTHh5Ox44d9b6WKXIOTZW3aAoiIiLw9/endOnSzJ07lxEjRmQWekgkRYh8/VxOH1fYCCFQFIVnnnmGtLQ01q5dS69evYwqkBJCsGrVKiZMmMDly5cB6N+/P599FkK1auUKOpgmxy4uDoBLN24wcdUqIvfsoVqFCnz18cfUuH1b0wIvI+1DS8JN3Z09cu1PS3v8b1OMIZFYOWZdAhZC/CWEuC+EeCSE+BbN0/FrOo5bKoTwEUL4uLi4mNNEi3P27Fl69+5N69atOXToEFWqVOHrr78mKiqqQMEfGJ9zaKoxjOHRo0fcvXsXgOeff56PP/6Y+Ph4AgICZPAnKZLo4+fM5eOSk5OZOnUqXbt2RQhBjRo1iI6ONroX7uHDh2ndujX9+/fn8uXLNG/enP3797Ny5UqqVatWsMFyVOPeuXePhv7+rDt4kMlvvUXM/Pn0a9MGm//+0xyXI+WlVsUHOofNtT/reTnaPxo0hkRi5Vg6B1AAUqMDuHfvHhMnTqR+/fqsXbuWkiVLEhQURFxcHAMGDDBI5sbQnENTj5GN9HQ4e1bjyHft0ryePZsruVoIwcaNG2nYsCHjxo0DNAHgokWLcHZ2NvDiEolFMLufE0KwevVq6tWrx7Rp0yhXrhwpKSmAcbJIV69eZcCAAbRo0YIDBw5QuXJlVqxYwcGDBw3Pz46ORly7xqEYTbF0eScnwt97j3/Dwwnp04fSJUtqjtNW46pU2foLh/Q5gaN9dv/haJ9OSJ8Tj3fY2kLWwLRaNc0+Y8Z4Enr6OYnEkpgtAFQUpbyiKK8oilJSURQ7RVF8gXbANnPZUBRJT09n6dKluLu7M2vWLB49eoSvry+xsbFMnz6d0qVLZzu+IBW5vr6wdCm4umr8paur5r0+S9CmHAPQPOWfOAE//ghHj8Lly5CYqHk9elSz/8QJEIKoqCjat29Pz549KVmyJL169SrgxSQSy1AU/FxCQgLt2rWjb9++uLi4sGfPHlavXo1jzie5AvDo0SNmz56Nh4cH33zzDSVKlGD8+PHExsbywQcfGF48kp5O9M6ddAoO5vnJkzkYG8uqPTWZtn4ubqOG667GvX49myafb9sLLB0ShatzMooicHVOZumQKHzb5ijYyKoekUNJwqAxdFEAPyeRWBpFmOkXUVEUF2ALUA9QAaeBT4QQvz3pPB8fHxEVFWUGC83Pb7/9hr+/P9EZ+lKtWrVi3rx5tGjRQufx2orcrEKrjo4GBmTmRLvEk5/mlq0tSw8eZMicObi4uDB9+nQ+/PBDozTDJE8viqIcEUL4mPmaBfZzpvJx2jy/5ORk2rVrx9ChQw1ePcg65i+//JKpOgDQrVs3wsPDcXd3N8reW7duMWXMGL5YtYpypUoxrXdvypX6kI+XteBB6uPvvKN9uu5gTF9sbcHdHby9s+8/cSKXFEyBx8hKAfwclSpB69bZZjIlkoJirI8zWwBoKMUxADx16hRjx45ly5YtANSuXZtZs2blm5dTu7ZGhiUnrq5w7lzh2GoS8nG0yQ8fcufBA6pXqEDCrVt8cfgwE8PCKFeugInkEkkWLBEAGoKxPi41NZVFixaxevVq9u3bh729fWYwaAynTp3Cz8+P7du3A1C/fn3mzZvHK6+8YtS4oFn58PDw4Pz58wzt1Ingd96hYpky1B72GucTc2t4ujonc+7zLQW/0JOCLVMHbKYOKCWSfDDWx1k6B9DqMEYUOTExkREjRuDt7c2WLVsoU6YMs2bN4tSpU7zzzjv5Omx9K3KLlHBzPoKrzgNfpMbQPXyweDEAtSpUIPS11ygnhZwlknzZtm0bjRo1IiAgABcXF+7cuQMYl+e3dGkSZcveokEDT7ZvX0KpUh8yb948jh07ZnTwd/DgQdRqNXZ2dsyZM4d/li5l0YcfUrFMGUD/atwnijZrsbHRBFl5BW6KovnM3V0TkOWcKbWzexyo5Rf8SWFpiRUi19YKQM4lWK0oMjx5CVb7hD5t2jTu3r2LjY0NQ4YMYdq0aVSqVEnv69eqpXsGMGtFrqE2Fhp5CK5ql3huJlXERplPK49taFbMspxXp44ZDZVIrIfbt2/Tv39/fvnlF9zd3fn555/p2rWrUWOqVCo++mgXK1a0ArQdO2oDy3BxUYwqgD179ixjx45l48aNrFmzht69e9OzZ0/NDFyGhAxoqm51zQBmrcbN6UO0os3A42ViGxt47jnIr52domhm4erX1/icy5c1Ui8lSmgKPmrW1ASC+ZGPn9Npo/Y86eckFkLOABaAvBqdBwbqPl4IwaZNm2jQoAEBAQHcvXuXTp068c8//7BkyZICBX+gX0VuQW0sdC5fzvZUHLjaO1t+D4BaOPLtn1lmFlSqbH8UJBKJBm3HDm1V79y5c4mOjjY6+Nu9ezc+Pj6sWOEGZHcyKSmKwf4jKSmJoKAg6tevz7Zt25gxYwZvvPHG4wMMqMbV5UMepNoRuDrLcqpaDdeu6W+onZ0mEGvdGjp00LzWqaNf8Ad6+blcNko/J7EwMgB8AjmXUnXNvoHupdm///6bjh070qNHD/777z/q16/Pli1b2L59O94G5n3oU5FblISbgUzB1TvJyUQnJEjBVYnEANRqNV999RUNGzYkMTERGxsbduzYQUBAAPb2hrdZi4hIxMnpBu3bt+GffzYBugU+DfUfr732GiEhIfTq1YuYmBgCAwMpqZV1AYOqcYukD5HC0hIrRC4B54GupVRF0V29n3UJ9tKlSwQGBrJy5UqEEFSsWJFp06bx0Ucf5dmztyDk10lEn2Vic5JuY8OX27Yxdd06KpcrR82KH5OQzxIPIAVXJZIM9u3bx+jRozly5AgtW7bkzp07ODs7G5Xn9+DBA/r338rGja8C2u9jbRRF5Ovj8uPIkSM0aNCAUqVKERwcjIODA61atdJ9sJ2dJscuS/6cb9sLT6z41WeZGDCvD9EhLF3kbJRIcmB1M4DmKnDQtZQqRO48YO0SbHJyMsHBwXh4ePDtt99iZ2dHQEAA8fHxDBs2zCTBnz6YXLjZCLZu3UqjQYMY8fXXeNeqReTIkcw0teCqRFLMeOzjBE5ON2jTZjFXr15l1apV7Nu3D7f88tqegBCCH374gXr16rFxYzMeB3/az5U8fVx+XL16lYEDB+Lj40NERAQAHTt2zDv40+Llpamy1VOuxuSizaagsIWlJZJCwKpmAM1Z4JDXkocQmqXXhATNU/GMGWpUqkg8PSdz6dIlAHr06MHs2bOpW7euaY3SA+19CAx8bGNIiPkLQLZs2ULXrl1xc3PjfxMm0L1pUxRFoUkdzZN94GpvEm46UqviA0L6nCi44KpEUgzR+DjBgwcKoPDggQt2dt8wbZqavn1L5nv+kzh69CijR49mz549GXt0T+vl9HH5+Y9Hjx4RERHB9OnTefjwIePGjWPo0KH6G6atxs3SCziblIqtbbb3Wl9RpHxIzZoaoeeibKNEkgOr0gE0RAdv1SrDgiF9rrVnzx78/f3R2te0aVPmzZtHu3bt8r9AMeTGjRtER0fTsWNHVCoVK1euxNfXF/uYGKmPJTE71qYDKISgUqUHJOrSwTPCx924cYOgoCCWLVuGEAJnZ2dmzpzJjBmDSEjIvYyc61rp6Y8rZFNTNcudWSpk+/fvT2RkJF27diU8PBwPDw/Db0bWa2Wtxr13D/77r2j7EKkDKDEzxvo4q5oBLGiBgzEzhiEhurtuhITAmTNnGD9+PBs2bACgWrVqzJw5k/79+xveEsmKefjwIREREYSEhFCqVCkSEhKwt7dnwIABmgO8vODuXf0FV728zGO4RFJEOH78OGPGjCExcYfOzw3xce+8k8aiRYsIDg7m7t272NnZMXLkSKZMmUL58uVxdMzbxwGaqcA8ZuVOHznCM6VLU7l5c8YGBODr68urr75qxB3IQFuNm1MaRQi4f79o+xDp5yRWhlVFK3klIue1X19JFF15hboqbhcseMA//4yjfv36bNiwgVKlSjF16lRiY2N5//33n7rgTwjBunXraNCgARMmTKB9+/b88ccfuasSTSm4KpEUMy5cuECTJk04duwYzzyTpPOYgvo4f/8UGjVqhL+/P3fv3sXbOxQXl2Tmzw+ncePyefq4TFUBbZcM7YxWRkBzJzkZ/2+/xdvfn0/XrIG4OJ5LSuJVE3QHeSLW4EOswUaJJAtWtQRc0F64Nja6q3YVRSMTpe+Y6enpLFu2jClTppCYmAhA//79mTlzJjVq1DDFj2mV/PXXX7zwwgs0atSIsLAwXn755fxPymuJR1/BVYlET6xlCbhq1aqiV69eBAcHs3VrBZP4OFADtri7u9O9+w988UXjjLzC/McEci1nqtRqvv79dwLXrCHx/n0GvfgiM959l0rlypl/OdMafIg12Cixeoz2cUKIIr01a9ZMZCUyUghXVyEURfMaGSnyxNVVCI17zL65uup/zNatW0WDBg0EIADRpk0bcfjwYYPsKQ4kJCSI77//PvP9li1bRHp6ugUtkkh0A0SJIuDD8tsKw8cpynkxe/Zs8ejRI738YDbS0oTYsEGItWszt+4+4QLOClCJquXviMiRB7J9LjZs0JwnkUjMhrE+zqpmAAuKPrN7ec8SCjp37pLZCP3ZZ59l9uzZ9OjRI1N/q6AzktZMUlISs2bNYu7cudjb25OQkEC5cuUsbZZEkifWMgNojI+LjFTz4YdqUrN0nbC1fURERArDhpUH9FsJycbZs3D0KAnXrvEoLY1D8S346MtmpKQ+lrJytE/PLtBsawtNmsi2ZhKJGTHWxxXrpDV9OmfklVsjRALbt2+nbNmyzJkzh3///ZeePXtmE18tcm3XCgGVSsWKFSvw8PBgxowZvPnmmxw/flwGfxKJhTlw4AALFjxPaup7wDlATZUqj/j2W4fM4A8Knjv94MwZgtesod6YMYxasYLA1d7Zgj+Qbc0kkuJAsQ4AQRPsnTunedI9dy73zJwu4WRIRlECGTZsGPHx8YwdOxYHB4dcYxe5tmuFwLlz5xg8eDCurq7s37+f1atX4+rqammzJJKnlkuXLtG/f39atWpFVFQU1ar9yXff7UWtVrhyxUEvH6dL3FkIwdq1a6nfty+frlvH682aseSjj2RbM4mkmFLsA8D86NtXMGjQX9jaXkSTOH2ORo0WEx09mcWLF+Pi4pLnuQV9srYW4uLimDNnDgB169blr7/+Yv/+/bRs2dLClkkkTy8PHz4kJCQEDw8PIiMjcXBwIDAwkJiYGPr165dnazh9VkIAli9fTu/evXmmbFn+/PRTfvDzw9XFJXf7sgxkWzOJxLqxSACoKIq7oigPFUWJtMT1tRw+fJh27doREfECKlVNGjTwZtu2GI4dG0+DBg3yPb8otV0zBbdv38bf35+GDRsybdq0zM4mTTO6eEgkEv0wpY8TQrBp0yYaNGhAUFAQDx48oEePHvz777/MmDGD0qVL5ztGXishN27c4GhGB4u+ffvy1VdfcWTrVtpDeRkPAAATJElEQVRlqeiVbc0kkuKJpWYAFwOHLXRtLl68SP/+/WnRogV79+7FxcWFL774gmPHjvFKAfSs9H2yLuqkpaWxcOFC3NzcmD9/Pu+//z5xcXFUr17d0qZJJNaKSXxcdHQ0nTp1okePHpw9exYvLy927tzJhg0bePbZZw0eNy0tjQULFuDh4YGvry9qtRonJycGDhyIbe3a2Y71bXuBpUOicHVORlEErs7J2QtAtMi2ZhKJVWF2QSJFUd4F7gD7AcO7mhtAUlISc+bMYc6cOaSkpGBvb8+YMWOYPHmywUUNvr7WF/Dl5P79+0ydOpUmTZoQHh7Oc889Z2mTJBKrxRQ+7ubNm0ydOpUvvvgCtVrNM888w/Tp0xkyZAh2RurI/frrr4wZM4ZTp07RuXNn5s+fn13E3s5Oo+uXRQfQt+2F3AGfFq0OoNS3k0isCrPOACqKUhaYBvjnc9xgRVGiFEWJunHjhkHXytrdw9VVMGTIn3h4eDBt2jRSUlLo1asXp06dYtasWU9lReuJEycYOXIkarWaChUqcPToUXbs2CGDP4nECIz1cenp6SxevBgPDw8WL16MoiiMGDGCuLg4hg8fni3409XBKD+2b9/OK6+8QmpqKps3b2bbtm3Ur18/94FeXpp2ZTm7WeREtjWTSKwWcy8BTwe+EkJcfNJBQoilQggfIYTPk4ow8kKrz3f+vEb/KiFBYelSH65c6YCPjw979uxh3bp1Ri2hWCtXr15l8ODBNG7cmFWrVhEbGwuAq6urzPOTSIzHYB/3+++/06RJE0aMGMGtW7d48cUX+eeff1i4cCEVK1bMdn5OH6ftAawrCLx//z779u0DoFOnTixfvpyTJ0/SvXv3vL/zsq2ZRFLsMZsQtKIojYFVQBMhRKqiKJ8CbkKIfk86zxCR1Nq1NQ4xJxUrJnH9uuNT17MX4NGjR4SFhREaGsrDhw8ZMWIEn3zyCRUqVLC0aRJJoWBuIWhDfZy3t7dwd3dn06ZNANSpU4ewsDDefPPNPAO0vHycq6umyANArVazcuVKJk2aRGpqKgkJCTg5ORX8B5NtzSSSIomxPs6c394OQG0gIcOplQZsFUVpIIRoaqqL3Llzh/PnywG5HeetW6V5CmM/ABRF4ZtvvuGll15i9uzZeHh4WNokiaS40QEDfNzJkyeJjo7GycmJyZMn4+/vT8mSJXMfmCUQS0hohS4fp9UgPXjwIKNGjeLw4cM8//zzREREGBb8gSbIq1NHdvmQSIoZ5gyHlgJ1gcYZ2xLgF0D/stsnkJaWxqJFi3BzcwN0PBpj/fp8BeXgwYO89dZbJCcnY29vz6FDh/jf//4ngz+JpHAwyMcJIejXrx8xMTFMnjw5d/AnBJw4AT/+CEePwuXLeWvz1YLTp0/TsmVLLl68yMqVK9m/fz8tWrQwwY8nkUiKE2YLAIUQD4QQV7UbkAQ8FEIYVuXxeFy2bNlCo0aNGDlyJDdv3qReve8oWVKV7Thr1ucrKAkJCfTt25eWLVty8OBBYmJiAChfvnw+Z0okEkMx1MfVq1eP7777TrfskhCwb9/jityMqlxd2nwOJdIICRHUq1ePyMhIYmNj6d+//1OZ8iKRSPLHYp5BCPFpfrkx+XHixAleeeUVunbtyunTp6lbty4bN27k33+DWL7c1ur1+QpKWloaQUFBeHp6smnTJoKCgoiNjaVpU5OtsEskEj3R18c9cWk2OhquX88M/LRotflqOSejILC1uUBq+gDaV9qh+dzXVy+BaIlE8vRilRm8165dY8qUKSxfvhy1Wk25cuWYMmUKFSuOws/Pjp49NUshISHFP+jLip2dHQcOHKBHjx6EhoZS62lb85ZIihPp6dm0+ABW7alJ4GpvEm46UqXcPZ4pPQ3BbOpVr8mCDz6gxv37mvNMWZyRtQgkNRXs7WURiERSDLCqb+/Dhw9ZsGABISEh3L9/H1tbW0aMGMHUqVPZvt2ZwYPhQUZqjFYWAYp3ELhz504++eQT1q5dS40aNdi6dSv29vaWNksikRjLhezCy6v21GTwlz48SNW47St3ynHlzhTea1+Xrz4ui51WquXCBdMUbAihmYGMi9O8zzoLee2aJh/R3V2jAShlYCQSq8MqkkOEEKxdu5b69eszceJE7t+/T9euXYmOjmbhwoU4OzsTGPg4+NPy4AEEBlrG5sImJiaG7t278/LLL3PlyhUuZPyxkMGfRFJMuHw5W9A1ebV3ZvD3GCf+POn7OPhTqTTnGUseuYeZaPfFxWmOM5OcmEQiMR1FPgBMTk6mTZs29O7dm3PnzuHt7c2vv/7Kzz//TL169TKP08of5CSv/daKEAJ/f3+8vLz4448/+Oyzzzh16hQtW7a0tGkSicSUpKZm/nNXdDQJiaV0HpZw0zH7jrQ046+dR+5hLlQqzXHR0cZfUyKRmJUiHwCePn2a/fv3U6lSJb788kuOHj1Kp06dch2XV7pbYafBGdKOyRDUajWg0fNLTk7mww8/JD4+ngkTJujWDJNIJNaNvT1nr1+n59y5vDhtGrY2l3QelksSpkQJ466bR+5h7WGvYdO7F7WHvcaqPTUfH6+dCUxP1zGYRCIpqhT5AFBRFCZNmkRcXByDBw/GNo/elCEhGqmXrBS29EtB2jEZihCCzZs306BBA7QdUZYsWcKSJUuoVKmS6S4kkUiKFGkuLrSdMoVtx44xvXdvlg05n0v6xdE+nZA+Jx7vsLXVFGgYQx65h+cTnRBC4XyiE4O/9MkeBOo4TyKRFG2KfADo5eXFzJkzKVu27BOP8/XVSL2YU/qlsPMO//nnH1566aXMllCPHj0CkD17JZJiivaBT6VSUeLZZ/lmxAhi5s8nqGdPBnS8wtIhUbg6J6MoAlfnZJYOicK3bY7Aq2ZN3YPrS47cw0AduYcPUu0IXO39eIepcg8lEonZKPJVwAUpavD1NW/Fb2HmHY4ZM4aIiAgqVKjAokWLGDx4MCWMXdqRSCRFlqioKEaNGsWBAwdYs2YNvXv35uVevbItx/q2vZA74NNia6upyjVWmiVL7iHoyDHMa78pcg8lEonZKPIzgEUZU+cdPnz4EJFRTVetWjX8/f2Jj49n+PDhMviTSIopaWlpDBw4kBYtWvDff//x1Vdf8fbbb2s+9PKCSpU0wd2TsLXVHOflZbxBOR6682w7Z+rcQ4lEYlZkAGgEpso7VKvVfP/993h4eLBhwwYAxo8fz9y5c2X7NomkmBMfH09kZCQBAQHExcUxcODAx+3bFAVat9bM7Nna5g4E7ewez/y1bm0aPb5q1bJdR1fbuULJPZRIJGalyC8BF2W0y82BgZplX0O6j+zfvx8/Pz8OHTpE06ZNdfcDlUgkxZZatWqxefNmPDw8dB+gKODtDfXrP+7IkZammXErjI4cNWtqRJ4z0C45azuQ1Kr4gJA+J0yfeyiRSMyKIoq4gKePj4/QVr8WN/z8/Jg/fz5Vq1YlNDRUNm6XSEyIoihHhBA+lrYjP4qkjztxIpcUTJ5oZyC9vfM/ViKRmAxjfZycATQz9+7dw8HBAQcHB1q2bEnZsmUZN26cbNwukUiKDl5ecPdu/mLQpsw9lEgkZkVON5mJ9PR0li5diru7OxEREQC88847BAcHy+BPIpEULSyReyiRSMyKnAE0A7/99hv+/v5ER0fTpk0bOnbsaGmTJBKJ5MmYO/dQIpGYFfntLWQmTJjA7NmzqVOnDuvXr6dHjx5SyFkikVgPdnZQp45mk0gkxQazBoCKokQCLwFOwFVgthBiuTltMAc3b97ExsaGZ555hjfeeANnZ2dGjRqFg4ODpU2TSCSFzNPi5yQSiXVj7hzAUKC2EKIs0B2YoShKMzPbUGikpqYSHh6Om5sbQUFBALRq1Ypx48bJ4E8ieXoo1n5OIpEUD8waAAohTgohHmnfZmx1zWlDYSCEYNOmTTRs2JCAgABeeOEFhg8fbmmzJBKJBSiufk4ikRQvzK4DqCjK58AHQCngKNBOCJGU45jBwOCMt15AtDltLOI4A4mWNqKIIO9FduT9yI6nEKKMJS6cn5+TPu6JyN/j7Mj78Rh5L7JjlI+ziBC0oii2QEugAzBLCJFnF3FFUaKsQczVXMj78Rh5L7Ij70d2LH0/9PVzlrazqCHvR3bk/XiMvBfZMfZ+WEQHUAihEkLsBWoAQy1hg0QikRQm0s9JJJKijKWFoO2QuTESiaR4I/2cRCIpcpgtAFQUpZKiKO8qilJaURRbRVFeAfoAO/M5dakZzLMm5P14jLwX2ZH3Iztmvx8G+jn5/5YdeT+yI+/HY+S9yI5R98NsOYCKorgA64Hn0ASe54EIIcQysxggkUgkhYz0cxKJxFqwSBGIRCKRSCQSicRyWDoHUCKRSCQSiURiZmQAKPl/e/cWKtdZhnH8/5iERBuiVmxAMQ0BRVu1FkEviqgIQsCQQD01oVSoB5AasCIINjamQmmwqFDpjWljRay9iNWK4I2IoN6IWCRaAqY21mChB8yBGGP7ejEr7J3dSbJPM9/sWf8frIs967t45+PdD+/MrJklSZJ6pvkAmOTKJD9JcjrJU0l2XmRdktyT5LnuuCdJxl3vqC1gP/YmOZfk1Kxjy7jrHaUktyX5Q5KzSQ5eZu0Xk/wryYkkDySZunvvzXc/knwqyYtzeuMD46t09JKsTXKg+x85meRPSbZeYn3T/jDnZphxFzLnZphxFxp1zjUfAIHvAv8FNgK7gPuTXDtk3WeBHQwurn4nsA343LiKHKP57gfAj6tq/azj6NiqHI/jwDeABy61qPum5VeADwFXA1uAr4+8uvGb1350fj+nN3492tLGbjXwD+D9wKuBO4BHkmyeu3BC+sOcm2HGXcicm2HGXWikOdd0AExyBXAjsKeqTnU/mvoz4OYhy28B7q2qp6vqn8C9DG61NDUWuB9Tr6oOVdWjwHOXWXoLcKC7B+sLwF1MWW/AgvZj6lXV6araW1V/r6qXqurnwJPAu4csb9of5twMM+7lzLkZZtyFRp1zrd8BfAvwv6o6Muuxx4Fhrwav7c5dbt1KtpD9ANiW5Pkkh5P0+U4Dw3pjY5LXNapnElyf5NkkR5LsSbK6dUGjlGQjg/+fw0NOt+4Pc26GGbd4rft40vQq42D5c671ALgeODHnsX8Dw25uvL47N3vd+im7PmYh+/EI8Dbg9cBngK8luWm05U2sYb0Bw/etD34DvB24isG7LTcBX25a0QglWQP8EPh+VT0xZEnr/jDnZphxi9e6jydJrzIORpNzrQfAU8CGOY9tAE7OY+0G4FRN1w8Zzns/quovVXW8u9/o74DvAB8dQ42TaFhvwPA+mnpVdbSqnuw+MvgzsI8p7Y0krwB+wOCastsusqx1f5hzM8y4xWvdxxOjTxkHo8u51gPgEWB1kjfPeuw6hr+9ebg7d7l1K9lC9mOuAqblXYKFGtYbz1SV15EMTGVvdO+KHWDwZYIbq+rcRZa27g9zboYZt3it+3iSTW1vjDLnmg6AVXUaOATsS3JFkhuA7Qwm3bkeAm5P8sYkbwC+BBwcW7FjsJD9SLI9yWu7n414D7Ab+Ol4Kx6tJKuTrANWAauSrLvIdR4PAbcmuSbJaxh8U+rgGEsdi/nuR5Kt3bUiJHkrsIcp643O/Qw+ItxWVWcusa5pf5hzM8y4lzPnZphxQ40u56qq6QFcCTwKnAaOATu7x9/H4KOP8+sC7Aee7479dLeym6ZjAfvxIwbflDoFPAHsbl37CPZiL4NXdrOPvcCm7nlvmrX2duAZBtcXPQisbV1/q/0AvtntxWngKIOPR9a0rn+Z9+Lq7vn/p3vu549dk9gf5tyi9mLqM657nubcAveiDxnXPc+R5pz3ApYkSeqZ1tcASpIkacwcACVJknrGAVCSJKlnHAAlSZJ6xgFQkiSpZxwAJUmSesYBUJIkqWccACVJknrGAVCSJKlnHAA10ZK8MsnTSY4lWTvn3PeSvJjkk63qk6SlMOPUigOgJloNbn59J/Am4PPnH09yN3Ar8IWqerhReZK0JGacWvFewJp4SVYBjwNXAVuATwPfAu6sqn0ta5OkpTLj1IIDoFaEJB8BHgN+BXwQuK+qdretSpKWhxmncXMA1IqR5I/A9cDDwM6a07xJPg7sBt4FPFtVm8depCQtkhmncfIaQK0IST4BXNf9eXJuMHZeAO4Dvjq2wiRpGZhxGjffAdTES/JhBh+NPAacAz4GvKOq/nqR9TuAb/vqWNJKYMapBd8B1ERL8l7gEPBbYBdwB/AScHfLuiRpOZhxasUBUBMryTXAL4AjwI6qOltVfwMOANuT3NC0QElaAjNOLTkAaiIl2QT8ksE1L1ur6sSs03cBZ4D9LWqTpKUy49Ta6tYFSMNU1TEGP4w67Nxx4FXjrUiSlo8Zp9YcADU1uh9TXdMdSbIOqKo627YySVo6M07LyQFQ0+Rm4MFZf58BngI2N6lGkpaXGadl48/ASJIk9YxfApEkSeoZB0BJkqSecQCUJEnqGQdASZKknnEAlCRJ6hkHQEmSpJ5xAJQkSeqZ/wPS0ZoRylSvVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `gamma=\"scale\"`, as this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=16)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=16, labelpad=10)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=16, labelpad=10)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=5)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x201.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12262d208>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.29825401306152344\n",
      "[LibSVM]1 0.01 0.27468299865722656\n",
      "[LibSVM]2 0.001 0.33706188201904297\n",
      "[LibSVM]3 0.0001 0.577028751373291\n",
      "[LibSVM]4 1e-05 0.9675290584564209\n",
      "[LibSVM]5 1.0000000000000002e-06 0.8886871337890625\n",
      "[LibSVM]6 1.0000000000000002e-07 1.0047168731689453\n",
      "[LibSVM]7 1.0000000000000002e-08 0.966433048248291\n",
      "[LibSVM]8 1.0000000000000003e-09 0.9602530002593994\n",
      "[LibSVM]9 1.0000000000000003e-10 0.9512200355529785\n"
     ]
    },
    {
     "data": {
      "image/png": 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dibQUmSSN24HLzexOQme/W5O27Qa8kc3ARKT59t4byspURSXZk0nSmAJcBbQjNIpfk7RtMPDPKAcxsx5mNsvMqsxsmZmlbQsxs3Zm9gcz+8jMVpvZXDPrk0G8IiWvdWsYOxbuuQfWaXQ4yYLIScPdN7j7VHc/3N0vcfcNSdvGuvu1EQ81DVgPlAHjgRvMbMc0+51OGKJkF8IAiZ8A10eNV0SCceOgqgoeeijuSKQlyGvnPjPrBBwJXOjule7+ODAHOC7N7t8AHnD3jxIj6P4dSJdcRKQBo0aFWf1URSXZYN7AvJBm9g4QeeJId9+mwZOZ7Qo84e4dk9adDYxw98NT9t0duA74DvAp8Gdghbufkea4E4GJAGVlZUPvuOOOqCEXpMrKSjp37hx3GAVD5VFXU8rj8su355lnejJz5pO0bt1y5oLV30ZdzSmPUaNGPe/uuze2X2PzacynbtI4gFCt9ATwUeL5MGA58EiEuJJv2631GdAlzb5vAO8R7traACwCTkt3UHefDkwH2H333X3kyJERQilcFRUVFPtnyCaVR11NKY9PPgnVU2YjaElFqb+NuvJRHg0mDXefUPs88Wt+L2Afd38/af3XgfuBpyKcrxLomrKuK5DuLvJphEb3nkAVcC5wXyIGEcnAwQdDhw6himr//eOORopZJm0a5wAXJScMAHd/D7iYMKdGY5YAbcwseT6xwcDiNPsOAW5299XuXk1oBN/TzLbIIGYRATp2hEMPDRMzbYw0ZrVIepkkja2B+m7aqwYavR3W3auAmcAlZtbJzIYBY4C/pdn938DxZtYtMWzJqcAH7q7h10SaoLwcPvgAnn027kikmGWSNF4BzjGz9skrzawD4SrklYjHOZUwv/gKQofBU9x9sZkNN7PKpP3OJiSpN4CPCYMjjssgXhFJMno0tG2ru6ikeRprCE92LnAP8K6Z3cumhvDDCNO/HhrlIO6+GhibZv0CQkN57etVhH4cIpIF3buH9oyZM+Gqq8LYVCKZyqRz3yPArsBDwHDgJ4nHB4HB7v5oTiIUkawpL4e33oJFi+KORIpVRp373P1Vdx/v7tu6e8fE4/fc/bVcBSgi2TNmTLjCmDUr7kikWGm6V5ESUlYG++6rdg1pukzaNDCzEcAxhLkz2qdsdnc/IFuBiUhulJfDmWfCm2/CdtvFHY0Um0zmCD8ZmEeYsa87YCmLrlpEisC4xD2IqqKSpsjkSuMs4DbgB+6+PkfxiEiO9esHQ4eGKqpzzok7Gik2mVwd9AFuUsIQKX7l5fD00/Df/8YdiRSbTJLG80CDo9iKSHGoraKaPTveOKT4ZJI0JgFnmNl+uQpGRPJj0CDYfnvdRSWZyyRpzCWMPzXPzNaY2bspy7IcxSgiOVBeDvPnw6pVcUcixSSThvBHyGBCJhEpbOXlcPnlMHcuTJgQdzRSLCInjeS5NUSk+O22G/TtG6qolDQkKvWtEClRZuFq48EHYU26adBE0sgoaZjZzmZ2p5l9bGY1icd/mNnOuQpQRHKnvByqq+G+++KORIpFJj3C9wCeAUYB/wJ+mXjcH3jazIbmJEIRyZl99oFevXQXlUSXSUP4FcDLwAHu/r+LWTPrAjyc2H5QdsMTkVxq3TqMfHv77bBuHbRPHVFOJEUm1VPfAq5IThgAiddXAXtnMzARyY/ycqishIcfjjsSKQaZJI3GbreNdDuumfUws1lmVmVmy8zs2Hr2u8/MKpOW9WamqWNEsmz//aFrV1VRSTSZJI1ngPMT1VH/Y2adgJ8BT0c8zjRgPWGq2PHADWa2Y+pO7n6ou3euXYAngX9mEK+IRLDZZnD44TBnDtTUxB2NFLpMksb5wI7AMjO7xcyuMrO/AkuBnYDJjR0gkWCOBC5090p3fxyYAxzXyPv6E6aWvSWDeEUkovLy0DN8wYK4I5FCl0nnvmfN7FvAL4CDgR7AasIcG5e6e5Sqo4FAjbsvSVq3EBjRyPuOBxa4+9J0G81sIjARoKysjIqKigihFK7Kysqi/wzZpPKoKxfl0aFDK9q1G8b113+I2ZtZPXYu6W+jrryUh7vnbSFcLSxPWXcSUNHI+94EJkQ5x9ChQ73YzZs3L+4QCorKo65clce4ce59+rhv2JCTw+eE/jbqak55AM95hO/YTPppfM3MBtazbaCZbRHhMJVA15R1XYF6+6Oa2b5Ab+DOqLGKSObKy8P8Gv/+d9yRSCHLpE3j94TZ+9I5M7G9MUuANmY2IGndYGBxA+/5PjDT3SsjRSkiTTJ6NLRpo7uopGGZJI19gQfq2fYgMKyxA7h7FTATuMTMOpnZMGAM8Ld0+5tZB+Ao4OYM4hSRJth883D77cyZ4BrPWuqRSdLYHPisnm2fAz0jHudUoAOwArgdOMXdF5vZcDNLvZoYC3xKaGwXkRwrL4c334SXX447EilUmSSN94G96tm2F/BhlIO4+2p3H+vundy9r7vflli/wEN/jOR9b3f3folGGhHJsTFjwui3qqKS+mSSNO4EzjOz0ckrE69/Dvwjm4GJSP717g3DhsGsWXFHIoUqk6RxCbAImGNm/zWzZ83sv4TOeYuAi3MRoIjkV3k5LFwIb70VdyRSiCInDXdfS+iEdxLwGKGtYT7wQ2BEYruIFLlx48KjrjYknUyGRsfdvwRuTCwi0gL17x+mgp05E84+O+5opNBkPN2rme1iZqeZ2UVm1juxbrvUgQxFpHiNGwdPPQUffBB3JFJoMukR3s7M/gm8CPyWMAbVVonNVxNhwEIRKQ7l5eFx9ux445DCk8mVxlTgQMKItGWAJW27jzCIoYi0AIMGwTe/qVtv5asySRrHABck+lWsTtn2DtA/W0GJSLzMwtVGRUUYMl2kViZJoyfwagPHadf8cESkUJSXw4YNMHdu3JFIIckkabxD/fOA7wm83vxwRKRQDB0KX/+6br2VujJJGrcAPzez8UDbxDo3s1GEUW51G65IC1JbRfXAA1CpMaYlIZOkcTVwD2FE2k8S6x4HHgbud/frsxybiMSsvByqq+G+++KORApFJtO9bgCONrNphDulegGrCAljfo7iE5EYDRsGX/tauIvqO9+JOxopBBn1CIcwGi2g6edFSkDr1mHk2zvugHXroH37uCOSuGXSuW+gme2Z9Lq9mV1hZnPN7LTchCcicSsvD20ajzwSdyRSCDJp0/gd8H9Jry8nTP+6FXCtmf04m4GJSGHYf3/o2lUd/STIJGkMBp4AMLNWwPHAz9x9KHAZMDH74YlI3Nq1g29/G+6+G2pq4o5G4pZJ0uhGaPgG2JUw/eudidcVwDZRDmJmPcxslplVmdkyMzu2gX13M7PHzKzSzD4ys9MziFdEsqS8PPQMf/zxuCORuGWSND4Ctks8Pwh4y93fS7zuDET9DTINWE8Yv2o8cIOZ7Zi6k5ltAdwP/JHQG3074MEM4hWRLDnkkNAIrioqySRpzAGuMLNfEdoy/pm0bWfg7cYOYGadgCOBC9290t0fTxz3uDS7/xR4wN1nuHu1u69x9/qGMRGRHOrUKSSOmTNh48a4o5E4ZXLL7c+B9oQ+GnMIo97WOoJoVwEDgRp3X5K0biFhRsBU3wIWmdmThKuMZ4Afu/u7qTua2UQSbSplZWVUVFRECKVwVVZWFv1nyCaVR11xlcf225cxe/Yg/vjH5xk0aE3ez5+O/jbqykt5uHveFmA4sDxl3UlARZp9lxCmlN2DkKx+CzzR2DmGDh3qxW7evHlxh1BQVB51xVUeq1a5t2nj/rOfxXL6tPS3UVdzygN4ziN8j2c8c18zVQJdU9Z1BdL9bPkCmOXu/3b3dcDFwD5m1i3HMYpIGj16wKhRcNddEH7XSSlqMGmY2Rwz2zXqwRId/n5qZj+qZ5clQBszG5C0bjCwOM2+/wGS/zT1ZyoSs623hjffDD3F+/eHGTPijkjyrbErjaXA02b2jJlNStwCW6cdxMy2MrOxZvYX4EPgh8AL6Q7m7lXATOASM+tkZsOAMYRBEFPdBIwzsyFm1ha4EHjc3T/L5AOKSHbMmBGGE4FwpbFsGUycqMRRahpMGu4+CdgBeBaYAvwbWGdmq83sQzP7AniPkAh2BM4AdnH3Zxs47KlAB2AFcDtwirsvNrPhZva/AZjd/VHgfMLIuisIjeH19ukQkdyaPBm++KLuurVrw3opHY3ePeXubwE/MbOzCJMw7UUYOqQ9obPfa8Bj7r4sygndfTUwNs36BYT+HsnrbgBuiHJcEcmtd79y32LD66VlymRo9PXA/MQiIiWmb99QJZVuvZSOfN89JSJFaupU6Nix7rp27cJ6KR1KGiISyfjxMH069OsXpoJt1So8P1YtjSVFSUNEIhs/HpYuDUOJXH89LFkS5hCX0qGkISJNcuKJoa/G5Mnq7FdKlDREpEk22wwuugheeEGj35YSJQ0RabLvfQ+++U248ELYsCHuaCQfMkoaZrarmc00s5VmVmNmuyXWX25mh+QmRBEpVG3awKWXwquvwm23xR2N5EPkpGFm+wJPAdsDt6W8dyNQ33hTItKCHXkkDBkCU6bA+vVxRyO5lsmVxpXAA4ThQn6asu0FYLdsBSUixaNVK7jsMnj7bbjxxrijkVzLJGnsBtyQGHc99V6JlcDXshaViBSVww6DvfcOVVWp41NJy5JJ0lgHdKxn25aARp8VKVFmcPnl8MEHcINGi2vRMkkajwNnmFnrpHW1Vxw/BB7NWlQiUnRGjoQDD4QrroA1hTEbrORAJknjQkIV1cLEcwe+b2bzCPN5X5z98ESkmEydCitXwm9+E3ckkiuRk4a7LwT2Az4CJgMGnJbYPMLdX89+eCJSTPbcE444An71K1i9Ou5oJBcy6qfh7i+4+wFAF2BroKu7j3L3F3MSnYgUnUsvDdVTv/xl3JFILjSpR7i7r3P3D9x9bbYDEpHitssucPTR8NvfwvLlcUcj2ZZpj/BBZnahmU03s1tSlr9GPEYPM5tlZlVmtszM0g6sbGZTzOxLM6tMWrbJJF4RicfFF0N1dWgUl5Yl8sx9ZnY8cCOhAXwFkNr3M+o4l9MS7y0DhgD3mNlCd1+cZt+/u/v3osYoIoVhwACYMAH+8Ac46yzN7teSZHr31N3A19y9j7t/I2Vp9CrAzDoBRwIXunuluz8OzAGOa1L0IlKwfvGL8HjJJfHGIdkV+UoD6A38yN0/bcb5BgI17r4kad1CYEQ9+x9uZquBD4HfuXvabkNmNhGYCFBWVkZFRUUzQoxfZWVl0X+GbFJ51FVM5fHtb2/HTTf1YeTIZ9l66+x3FS+mssiHvJSHu0dagPuB06LuX88xhgPLU9adBFSk2XcHYCugNbAPIXEc09g5hg4d6sVu3rx5cYdQUFQedRVTeSxf7t6xo/sxx+Tm+MVUFvnQnPIAnvMI3+OZVE+dBkw0s2PMrKeZtUpdIhyjEuiasq4r8JX+o+7+ioc7tDa4+5PAdcD/ZRCviMSsrAwmTYLbb4f//CfuaCQbMkka7wMvArcSGsK/TFmiDIq8BGhjZgOS1g0G0jWCp3JCh0IRKSLnnAPduoWJmqT4ZdKm8Sfgu8Bs4DWiJYk63L3KzGYCl5jZiYS7p8YQqp/qMLMxwGPAp8AewCTg/EzPKSLx6tEDzj47JI1nnoG99oo7ImmOTJLGGOAcd7+umec8lXDr7gpgFXCKuy82s+HAfe7eObHf0Yn92hGucq5y90h9QUSksJx+Olx3HVxwATz0UNzRSHNkkjSqgFeae0J3Xw2MTbN+AdA56fUxzT2XiBSGLl3gvPNCn41582DUqLgjkqbKpE3jJiBt720Rkcaccgr06QOTJ4NH7QosBSeTK41lwDFm9hDh9ttPUndwd032KCJpdegQ2jV+9CO4914YPTruiKQpMkkatR3r+gEHpNnuhDYIEZG0fvADuPrq0LZx6KFhfnEpLpkkjW/kLAoRKQlt28KUKXD88XDnnXDUUXFHJJnKZBKmZY0tuQxURFqGY4+FHXYIY1PV1MQdjWRKF4cikletW4eJml5/HW69Ne5oJFMNJg0ze9vMBieev5N4Xd/yVn5CFpFiN24cDB0aqqqqq+OORjLRWJvGfODzpOe6UU5Ems0MLrssNIb/+c/w4x/HHZFE1WDScPfq/5iXAAAP9UlEQVQTkp5PyHk0IlIyDj4Y9t03JI8TToCOHeOOSKKIXD0lIpJNZnD55WEe8WnT4o5GomqsIbw/YewnEZGsGz48XHFceSV8/nnj+0v8dPeUiMTqsstg9Wq49tq4I5EooiQNNX6LSM7svnu4m+rXv4ZVq+KORhoTpUf4xWa2MsJ+7u7fb25AIlJ6Lr0UZs+Gq64Kw4xI4YqSNIYAUe6k1hWJiDTJjjvC+PHwu9/BmWfCllvGHZHUJ0r11Fh3/0aEZZucRysiLdaUKfDllzB1atyRSEPUEC4iBWHbbcMouNOnw9KlcUcj9cl70jCzHmY2y8yqzGyZmTU4sZOZbWZmr5rZ+/mKUUTiceGFYbj0iy+OOxKpTxxXGtOA9UAZMB64wcx2bGD/c4CP8xGYiMRr663h1FPhllvgtdfijkbSaTBpuHsrd382Wyczs07AkcCF7l7p7o8Dc4Dj6tn/G8D3gCuyFYOIFLaf/zzM8nfRRXFHIulkMglTNgwEatx9SdK6hcCIeva/Hjgf+KKhg5rZRGAiQFlZGRUVFc2PNEaVlZVF/xmySeVRVymUx7hx3+DWW/tx4IHPMWBAZb37lUJZZCIv5eHueVuA4cDylHUnARVp9h0H3Jd4PhJ4P8o5hg4d6sVu3rx5cYdQUFQedZVCeXzyiXv37u6jRze8XymURSaaUx7Acx7hOzbfbRqVQNeUdV2BNckrEtVYVwOT8hSXiBSQ7t3h3HPhnnvgqafijkaS5TtpLAHamNmApHWDgcUp+w0gDJa4wMyWAzOBLc1suZn1z0OcIhKzSZOgVy84/3xwdR0uGHlNGu5eRUgAl5hZJzMbBowB/pay68vA1wm90YcAJwIfJZ6/l7+IRSQunTqFhFFRAY88Enc0UiuOW25PBToAK4DbgVPcfbGZDTezSgB3r3H35bULsBrYmHi9IYaYRSQGJ58MX/86TJ6sq41Ckfek4e6r3X2su3dy977uflti/QJ371zPeyrcfev8RioicWvfHn7xC3j2WZg7N+5oBDSMiIgUuO9/H7bbDi64ADZujDsaUdIQkYLWtm0YVmTRIvj73+OORpQ0RKTgHX007Lxz6CVeUxN3NKVNSUNECl6rVmGipjfegL/+Ne5oSpuShogUhSOOgD33DFVV1VGmhZOcUNIQkaJgBpddBu+9B3/8Y9zRlC4lDREpGgceCCNHhtn9qqrijqY0KWmISNEwCwljxYow98b++4+gf3+YMSPuyEpHvodGFxFplnfeCQ3jn34KYCxbBhMnhm3jx8cZWWnQlYaIFJXJk7/ayW/t2rBeck9JQ0SKyrvvZrZesktJQ0SKSt++6ddvthk89lh+YylFShoiUlSmToWOHeuua9sW2rWDESPggAOUPHJJSUNEisr48TB9OvTrB2ZOv35w003w4YdwzTWweHFIHvvvr+SRC0oaIlJ0xo+HpUvh0Ufns3RpeN2xI5x5Jrz9Nlx7LbzyyqbkMX9+3BG3HEoaItKidOwIZ5xRN3mMHKnkkS1KGiLSIqUmj1dfDclj1Cglj+bIe9Iwsx5mNsvMqsxsmZkdW89+Z5rZ22b2uZl9YGbXmpk6I4pIRpKTx29+A6+9puTRHHFcaUwD1gNlwHjgBjPbMc1+c4Dd3L0rsBMwGJiUtyhFpEXp0AFOPz198qioiDu65pkxA/r3z8+wKnlNGmbWCTgSuNDdK939cUJyOC51X3d/y90/rX0rsBHYLm/BikiLlC55jBoVEkgxJo8ZM8IwKsuWgfumYVVylTjM3XNz5HQnM9sVeMLdOyatOxsY4e6Hp9n/WOAPQBdgJXCguy9Ms99EYCJAWVnZ0DvuuCNHnyA/Kisr6dy5c9xhFAyVR10qj02yURbV1a2YO3dLbr+9L6tXt2Pw4E+ZMGEpQ4Z82vibY7RuXSveeacTP/vZLqxZ0/Yr28vK1nHHHU9HPt6oUaOed/fdG93R3fO2AMOB5SnrTgIqGnnfAOBSoHdj5xg6dKgXu3nz5sUdQkFRedSl8tgkm2Wxdq37dde5b7mlO7iPGOFeKEW9YoX7gw+6X321+zHHuA8a5N6qVYizvsUss3MAz3mE7/F8t2lUAl1T1nUF1jT0Jnd/A1gM/D5HcYlIievQASZNgrfeguuugyVL8l9ttXFjOP9dd8EFF8C3vx2GgO/VCw46CM49F554AgYMCNtnzYI+fdIfq77hVpor33cjLQHamNmARCKA0MC9OMJ72wDb5iwyERE2JY+TToI//QmuvDIkj/32gylTQhIxa/55qqtD7/WXXqq7rEn8hG7dGgYNCufedVcYMgQGD4aePesep6oqtGGsXbtpXceOYbiVXMhr0nD3KjObCVxiZicCQ4AxwD6p+ya2z3H3FWa2A3Ae8EA+4xWR0pWcPP78Z7jiitBBMDl53HZbGJL93XfDL/upU9PP6fHJJ7BwYUgKL74YHl95BWpqwvZOnUJCOP74kByGDIGddoL27RuPs/Z8IQ6nb1+rN45siKPfw6nAjcAKYBVwirsvNrPhwH3uXtuqNQyYamadgY+BfwIXxhCviJSwDh3gJz/ZdOVRmzy++c0wlEl1ddiv9q6lVavCuFjJVw9Ll246Xu/e4cph9OiQHHbdFbbdNkws1VTjx4elomI+I0eObManbVzek4a7rwbGplm/AOic9PqEfMYlItKQ9u3rJo8zzkg/GdTpp4fnZjBwIOy1F5x8ckgOgweHpFHM1MNaRCQDtcmjNjmk8+STsPPO0BLvjNbYUyIiTVDf3Un9+sHee7fMhAFKGiIiTZJuMqhc3rVUKJQ0RESaoO5kUOFx+vTc3bVUKNSmISLSRLV3LZUSXWmIiEhkShoiIhKZkoaIiESmpCEiIpEpaYiISGR5nYQpH8zsY2BZ3HE00xaESackUHnUpfLYRGVRV3PKo5+7f62xnVpc0mgJzOw5jzKDVolQedSl8thEZVFXPspD1VMiIhKZkoaIiESmpFGYpscdQIFRedSl8thEZVFXzstDbRoiIhKZrjRERCQyJQ0REYlMSaNImVlrM7vVzOaZ2Y1mVrIjFpvZ3mZWkViWmNm1cccUJzPrb2YfJ5VJo/fet2RmVmZmT5rZfDN71My2jDumuJhZNzN71swqzWynphxDSaN4jQPecfdRwGtAeczxxMbdn3L3ke4+EngSmB1zSIVgfm2ZuPvHcQcTs5XAvu4+ArgF+GHM8cRpLTAauLOpB1DSKF7bAi8lnr8A7BdjLAXBzDYD9gQWxB1LARhmZgvM7HIzs7iDiZO7b3D3jYmXXYDFccYTJ3f/srk/IpQ08sDMTjOz58ys2sxuTtnWw8xmmVmVmS0zs2MjHvYVYP/E8wOBzbMYcs7kqCxqHQg8kvQFUfByVB4fAtsRfkj0ooiuQnP192FmQ8zsGeA0wo+sgpfj/ytNVrL14Hn2AXAZcDDQIWXbNGA9UAYMAe4xs4XuvtjMegN3pDne0cC/gJFm9ijhl9PyXAWfZVkvC3ev/ezfAW7KTdg5k6vyqAYws5nAt4C7chR/tuWkPNz9JWAvMzsKOA/4Uc4+Qfbk8v9K07m7ljwtiT+Am5Ned0r8ww9MWvc34MoMjzsF2C/uzxdnWQBtgZeBVnF/trjLA+iS9PwK4Pi4P1/M5bFZ0vODgWvi/nxxlUXS/jcDOzUlHlVPxWsgUOPuS5LWLQR2bOyNZtY7cefUI8B6d38sV0HmSZPLIuFA4FEvoqqpRjSnPPY1s+fNbAHQB7gtFwHmWXPKY4iZPWZm84AzgF/mIsA8atb/FTO7FzgI+JOZTcj05Kqeildn4POUdZ8RGusa5OEyc1QugopJk8sCwN3vA+7LdlAxas7fRksrC2heeTxLy7pRpLn/Vw5rzsl1pRGvSqBryrquwJoYYombyqIulUddKo9NYi0LJY14LQHamNmApHWDKc1bAlUWdak86lJ5bBJrWShp5IGZtTGz9kBroLWZtTezNu5eBcwELjGzTmY2DBhDaNRqkVQWdak86lJ5bFKwZRH3nQGlsBDubvKUZUpiWw9CD+Yq4F3g2LjjVVmoPFQe8S+FWhYaGl1ERCJT9ZSIiESmpCEiIpEpaYiISGRKGiIiEpmShoiIRKakISIikSlpiIhIZEoaIiISmZKGtChm5hGWpRkec7mZ/SFHIYsUFQ2NLi3N3imvZxHmGpiStK46b9GItDBKGtKiuPvTya/NrBpYmbo+LmbWzt2VtKRoqXpKSpqZnWBmi8ys2sw+NrObzKxXhPdtZ2Z3mNlKM1uXmClvdMo+V5pZjZntZGaPmFklcEti22Fmdn+i6qsqEcMkM2uVcozlZvZnMzvezF5P7PuMme2VJqYDE+f53MwqzewlMzs+abuZ2Y8T51pnZivM7I9m1q3JBSglR0lDSpaZTQJuBF4CxgIXAEcA88ysQwPv2wZ4BtgemEQYlvoVYI6ZHZy6O3A38CBwODAtsX4b4H5gQmL9DOBK4BdpTnkgcApwHnAM0BG4x8w6J8V0FPBA4uVJic/zV6Bf0nGuTSz3Jj7neYnY/5WarETqFffwv1q05HIBlgK3plm/GbAKuD9l/YGEIagnJq1bDvwh6fUM4AOgW8p7HwOeTnp9ZeJYJzcSoxGqii8FPkrZthz4GOiatG7fxHHLE69bJ+J5AsLI1WnOMRDYCJybsv6AxLEOifvfSktxLPp1IaVqJ8KcBLcmr3T3h4GPgBENvPcQYC5QlZgop42ZtSFcTeyRmDgn2azUA5jZ1mb2FzN7F/gysVwA9DKz7im7L3D35DmhFyUe+yYedwa2BP7k7vXNdXAwITnNSIn5McKNAS1pDm3JITWES6nqkXj8MM225Unb6zCz1oltExNLOpsnHXeju69IOUYb4B6gO3Ax8DqwDjgKOAdITTqrU17XNqTX7tcz8fh+PfEA9Gpkn571rBepQ0lDSlXtF3HvNNt6U898y+6+wcw+I3zpX1vPsVcmvyXN9kHALsB33P3O2pVm9p3Ggm7kfH0a2GdV4nEkYba3VB838dxSYpQ0pFS9TEgcRxPaKAAwswOAMqCigffeDwwGFnnTbp/tmHj8Mum87QiN3E3xMqFN4yQzu6WeKqoHCQlsa3efkWa7SCRKGlKS3H29mV0MXGdmNwF/J7QRTCXcCXVrA28/n3D3VIWZ/Z4wR/PmhKuHrdz9R42c/j+EL/mrk+5aOgtY38TPssHMzgTuAB40sz8Rrix2JDSgX+bur5jZb4DpZrYTsIBQzdUXOAi43t2fbMr5pbQoaUjJcvffJvpOnAkcC3xOqHY6192/aOB9b5vZ7oRe5lcBWxCqiBYBN0U47xdmNga4nnCVswqYnnic1tB7GzjmP8zsU2ByIoaNwJvAr5P2+amZvUy4ffd0YAMh4T0CvNOU80rpsfpvthAREalLt9yKiEhkShoiIhKZkoaIiESmpCEiIpEpaYiISGRKGiIiEpmShoiIRKakISIikf1/uF+r9X0r9dsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times, \"bo-\")\n",
    "plt.xlabel(\"Tolerance\", fontsize=16)\n",
    "plt.ylabel(\"Time (seconds)\", fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56761653] [[[2.28120287]\n",
      "  [2.71621742]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[2.27128546 2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.32885908   2.27493198   1.98084355]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha=0.017, max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28474532] [[1.05364923 1.09903601]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.117] [[0.77666262 0.72787608]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        max_iter=1000, tol=1e-3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"].astype(np.uint8)\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first. However, the dataset is already shuffled, so we do not need to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!\n",
    "\n",
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.895"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, 89.5% accuracy on MNIST is pretty bad. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we cut the error rate by about 25%), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof we set `gamma=\"scale\"` since it will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(gamma=\"scale\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94535"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\n",
      "[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.3s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.1s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.1s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.3s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   55.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c828>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c320>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2, cv=3)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.865"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99965"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.971"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: lbfgs failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.954517044073374"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976993881287582"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.4s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.3s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.2s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.1s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.7s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.2s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.8s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.9s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   5.1s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.7s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   5.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2e390>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2eac8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, cv=3, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785359"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528734"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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